{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入第三方包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:52:16.629173Z",
     "start_time": "2021-03-15T00:52:16.621194Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import math\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb   ####重点需要了解\n",
    "from catboost import CatBoostRegressor  ####重点需要了解\n",
    "from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "print(\"love_han\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:52:22.085956Z",
     "start_time": "2021-03-15T00:52:19.571864Z"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   id                                  heartbeat_signals  label\n",
       "0   0  0.9912297987616655,0.9435330436439665,0.764677...    0.0\n",
       "1   1  0.9714822034884503,0.9289687459588268,0.572932...    0.0\n",
       "2   2  1.0,0.9591487564065292,0.7013782792997189,0.23...    2.0\n",
       "3   3  0.9757952826275774,0.9340884687738161,0.659636...    0.0\n",
       "4   4  0.0,0.055816398940721094,0.26129357194994196,0...    2.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>heartbeat_signals</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.9912297987616655,0.9435330436439665,0.764677...</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.9714822034884503,0.9289687459588268,0.572932...</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1.0,0.9591487564065292,0.7013782792997189,0.23...</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.9757952826275774,0.9340884687738161,0.659636...</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.0,0.055816398940721094,0.26129357194994196,0...</td>\n      <td>2.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "path_train = r'E:\\Data_Whale\\心脏分类\\train.csv'\n",
    "path_test = r'E:\\Data_Whale\\心脏分类\\testA.csv'\n",
    "train = pd.read_csv(path_train)\n",
    "test=pd.read_csv(path_test)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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     ]
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    print(len(train.values[0][1].split(',')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "          0         1         2         3         4         5         6    \\\n0    0.865243  0.980797  0.636582  0.729560  0.438145  0.663434  0.064361   \n1    0.102753  0.673347  0.752252  0.721399  0.232542  0.356528  0.446144   \n2    0.201192  0.567514  0.622238  0.529515  0.731063  0.010569  0.478320   \n3    0.042193  0.679968  0.601708  0.368150  0.922416  0.588642  0.050744   \n4    0.190154  0.954190  0.943574  0.379251  0.004871  0.316208  0.644596   \n..        ...       ...       ...       ...       ...       ...       ...   \n995  0.583592  0.282558  0.996993  0.505914  0.961644  0.706422  0.536404   \n996  0.672258  0.444798  0.166212  0.875545  0.798330  0.508466  0.885402   \n997  0.038594  0.559583  0.260660  0.906384  0.332322  0.412381  0.306754   \n998  0.922579  0.406797  0.533406  0.296348  0.648266  0.256968  0.911393   \n999  0.667159  0.763154  0.158234  0.902987  0.403145  0.585787  0.378042   \n\n          7         8         9    ...       195       196       197  \\\n0    0.362288  0.758352  0.774990  ...  0.488416  0.119122  0.561581   \n1    0.627855  0.613758  0.565525  ...  0.558688  0.027810  0.077429   \n2    0.896998  0.853437  0.537892  ...  0.735928  0.174240  0.189145   \n3    0.107927  0.085353  0.532141  ...  0.103879  0.202493  0.409633   \n4    0.995626  0.056922  0.464953  ...  0.826360  0.417861  0.973438   \n..        ...       ...       ...  ...       ...       ...       ...   \n995  0.313786  0.595992  0.017202  ...  0.457176  0.248115  0.740644   \n996  0.352959  0.171781  0.911467  ...  0.395011  0.604997  0.479772   \n997  0.463792  0.580240  0.545971  ...  0.797437  0.699538  0.842808   \n998  0.191033  0.373394  0.836293  ...  0.322443  0.094119  0.297249   \n999  0.794879  0.696640  0.693600  ...  0.201086  0.532292  0.087378   \n\n          198       199       200       201       202       203       204  \n0    0.729728  0.427663  0.479420  0.061154  0.447055  0.352424  0.937105  \n1    0.991315  0.173783  0.873253  0.860155  0.413109  0.979061  0.963844  \n2    0.970100  0.652460  0.233877  0.719526  0.149536  0.205552  0.880581  \n3    0.818868  0.125768  0.749363  0.568214  0.353837  0.149435  0.094728  \n4    0.868127  0.671056  0.677038  0.135795  0.634047  0.283285  0.809900  \n..        ...       ...       ...       ...       ...       ...       ...  \n995  0.830512  0.962273  0.974424  0.202149  0.686931  0.075898  0.825928  \n996  0.013653  0.057774  0.462904  0.738666  0.705260  0.876567  0.653614  \n997  0.282718  0.403839  0.220546  0.637977  0.274968  0.182255  0.922664  \n998  0.685305  0.893270  0.580157  0.945585  0.507948  0.737144  0.438652  \n999  0.078107  0.575157  0.912504  0.352667  0.462820  0.917757  0.189101  \n\n[1000 rows x 205 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>195</th>\n      <th>196</th>\n      <th>197</th>\n      <th>198</th>\n      <th>199</th>\n      <th>200</th>\n      <th>201</th>\n      <th>202</th>\n      <th>203</th>\n      <th>204</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.865243</td>\n      <td>0.980797</td>\n      <td>0.636582</td>\n      <td>0.729560</td>\n      <td>0.438145</td>\n      <td>0.663434</td>\n      <td>0.064361</td>\n      <td>0.362288</td>\n      <td>0.758352</td>\n      <td>0.774990</td>\n      <td>...</td>\n      <td>0.488416</td>\n      <td>0.119122</td>\n      <td>0.561581</td>\n      <td>0.729728</td>\n      <td>0.427663</td>\n      <td>0.479420</td>\n      <td>0.061154</td>\n      <td>0.447055</td>\n      <td>0.352424</td>\n      <td>0.937105</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.102753</td>\n      <td>0.673347</td>\n      <td>0.752252</td>\n      <td>0.721399</td>\n      <td>0.232542</td>\n      <td>0.356528</td>\n      <td>0.446144</td>\n      <td>0.627855</td>\n      <td>0.613758</td>\n      <td>0.565525</td>\n      <td>...</td>\n      <td>0.558688</td>\n      <td>0.027810</td>\n      <td>0.077429</td>\n      <td>0.991315</td>\n      <td>0.173783</td>\n      <td>0.873253</td>\n      <td>0.860155</td>\n      <td>0.413109</td>\n      <td>0.979061</td>\n      <td>0.963844</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.201192</td>\n      <td>0.567514</td>\n      <td>0.622238</td>\n      <td>0.529515</td>\n      <td>0.731063</td>\n      <td>0.010569</td>\n      <td>0.478320</td>\n      <td>0.896998</td>\n      <td>0.853437</td>\n      <td>0.537892</td>\n      <td>...</td>\n      <td>0.735928</td>\n      <td>0.174240</td>\n      <td>0.189145</td>\n      <td>0.970100</td>\n      <td>0.652460</td>\n      <td>0.233877</td>\n      <td>0.719526</td>\n      <td>0.149536</td>\n      <td>0.205552</td>\n      <td>0.880581</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.042193</td>\n      <td>0.679968</td>\n      <td>0.601708</td>\n      <td>0.368150</td>\n      <td>0.922416</td>\n      <td>0.588642</td>\n      <td>0.050744</td>\n      <td>0.107927</td>\n      <td>0.085353</td>\n      <td>0.532141</td>\n      <td>...</td>\n      <td>0.103879</td>\n      <td>0.202493</td>\n      <td>0.409633</td>\n      <td>0.818868</td>\n      <td>0.125768</td>\n      <td>0.749363</td>\n      <td>0.568214</td>\n      <td>0.353837</td>\n      <td>0.149435</td>\n      <td>0.094728</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.190154</td>\n      <td>0.954190</td>\n      <td>0.943574</td>\n      <td>0.379251</td>\n      <td>0.004871</td>\n      <td>0.316208</td>\n      <td>0.644596</td>\n      <td>0.995626</td>\n      <td>0.056922</td>\n      <td>0.464953</td>\n      <td>...</td>\n      <td>0.826360</td>\n      <td>0.417861</td>\n      <td>0.973438</td>\n      <td>0.868127</td>\n      <td>0.671056</td>\n      <td>0.677038</td>\n      <td>0.135795</td>\n      <td>0.634047</td>\n      <td>0.283285</td>\n      <td>0.809900</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>0.583592</td>\n      <td>0.282558</td>\n      <td>0.996993</td>\n      <td>0.505914</td>\n      <td>0.961644</td>\n      <td>0.706422</td>\n      <td>0.536404</td>\n      <td>0.313786</td>\n      <td>0.595992</td>\n      <td>0.017202</td>\n      <td>...</td>\n      <td>0.457176</td>\n      <td>0.248115</td>\n      <td>0.740644</td>\n      <td>0.830512</td>\n      <td>0.962273</td>\n      <td>0.974424</td>\n      <td>0.202149</td>\n      <td>0.686931</td>\n      <td>0.075898</td>\n      <td>0.825928</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>0.672258</td>\n      <td>0.444798</td>\n      <td>0.166212</td>\n      <td>0.875545</td>\n      <td>0.798330</td>\n      <td>0.508466</td>\n      <td>0.885402</td>\n      <td>0.352959</td>\n      <td>0.171781</td>\n      <td>0.911467</td>\n      <td>...</td>\n      <td>0.395011</td>\n      <td>0.604997</td>\n      <td>0.479772</td>\n      <td>0.013653</td>\n      <td>0.057774</td>\n      <td>0.462904</td>\n      <td>0.738666</td>\n      <td>0.705260</td>\n      <td>0.876567</td>\n      <td>0.653614</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>0.038594</td>\n      <td>0.559583</td>\n      <td>0.260660</td>\n      <td>0.906384</td>\n      <td>0.332322</td>\n      <td>0.412381</td>\n      <td>0.306754</td>\n      <td>0.463792</td>\n      <td>0.580240</td>\n      <td>0.545971</td>\n      <td>...</td>\n      <td>0.797437</td>\n      <td>0.699538</td>\n      <td>0.842808</td>\n      <td>0.282718</td>\n      <td>0.403839</td>\n      <td>0.220546</td>\n      <td>0.637977</td>\n      <td>0.274968</td>\n      <td>0.182255</td>\n      <td>0.922664</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>0.922579</td>\n      <td>0.406797</td>\n      <td>0.533406</td>\n      <td>0.296348</td>\n      <td>0.648266</td>\n      <td>0.256968</td>\n      <td>0.911393</td>\n      <td>0.191033</td>\n      <td>0.373394</td>\n      <td>0.836293</td>\n      <td>...</td>\n      <td>0.322443</td>\n      <td>0.094119</td>\n      <td>0.297249</td>\n      <td>0.685305</td>\n      <td>0.893270</td>\n      <td>0.580157</td>\n      <td>0.945585</td>\n      <td>0.507948</td>\n      <td>0.737144</td>\n      <td>0.438652</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>0.667159</td>\n      <td>0.763154</td>\n      <td>0.158234</td>\n      <td>0.902987</td>\n      <td>0.403145</td>\n      <td>0.585787</td>\n      <td>0.378042</td>\n      <td>0.794879</td>\n      <td>0.696640</td>\n      <td>0.693600</td>\n      <td>...</td>\n      <td>0.201086</td>\n      <td>0.532292</td>\n      <td>0.087378</td>\n      <td>0.078107</td>\n      <td>0.575157</td>\n      <td>0.912504</td>\n      <td>0.352667</td>\n      <td>0.462820</td>\n      <td>0.917757</td>\n      <td>0.189101</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 205 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# import cufflinks as cf\n",
    "dd = pd.DataFrame(np.random.rand(1000,205),columns = np.arange(205))\n",
    "display(dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                    0                     1                    2    \\\n",
       "0    0.9912297987616655    0.9435330436439665   0.7646772997256593   \n",
       "1    0.9714822034884503    0.9289687459588268   0.5729328050711678   \n",
       "2                   1.0    0.9591487564065292   0.7013782792997189   \n",
       "3    0.9757952826275774    0.9340884687738161   0.6596366611990001   \n",
       "4                   0.0  0.055816398940721094  0.26129357194994196   \n",
       "..                  ...                   ...                  ...   \n",
       "995                 1.0    0.8830660530039279   0.5080689094453772   \n",
       "996  0.9752957033912956    0.9740493249054155   0.5560994002301956   \n",
       "997  0.5005756139651654    0.4242256882624317   0.3851693237279527   \n",
       "998                 1.0    0.9346402661958418   0.7685675963675491   \n",
       "999                 1.0    0.8825708964631178  0.39575843957964296   \n",
       "\n",
       "                      3                    4                    5    \\\n",
       "0      0.6185708990212999   0.3796321642826237  0.19082233510621885   \n",
       "1      0.1784566262750076   0.1229615224365985  0.13236021729815928   \n",
       "2     0.23177753487886463                  0.0  0.08069805776387916   \n",
       "3      0.2499208267606008  0.23711575621286213  0.28144491730834825   \n",
       "4     0.35984696254197834  0.43314263962884686  0.45369772898632504   \n",
       "..                    ...                  ...                  ...   \n",
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     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "for i in range(1000):\n",
    "    dd.iloc[i,:] = train.values[i][1].split(',')\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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     },
     "metadata": {}
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "# dd = pd.DataFrame(train.values[0][1].split(','))\n",
    "# display(dd)\n",
    "# int\n",
    "for i in range(100):\n",
    "    if (train['label'][i]) ==1:\n",
    "        dd.loc[i,:].iplot(mode='lines+markers',theme='solar',title='label:'+str(int(train['label'][i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.0    64327\n3.0    17912\n2.0    14199\n1.0     3562\nName: label, dtype: int64\n"
     ]
    },
    {
     "output_type": "execute_result",
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      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x232f29fa6a0>,\n",
       "  <matplotlib.patches.Wedge at 0x232f29fabe0>,\n",
       "  <matplotlib.patches.Wedge at 0x232f2a1ae10>,\n",
       "  <matplotlib.patches.Wedge at 0x232f2a1a4e0>],\n",
       " [Text(-0.47855720111059574, 0.9904458618547473, '0.0'),\n",
       "  Text(-0.11844033957045504, -1.0936049953993605, '3.0'),\n",
       "  Text(0.8622846641815177, -0.6829825458365444, '2.0'),\n",
       "  Text(1.0931198841388337, -0.1228369606441928, '1.0')])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "print(train['label'].value_counts())\n",
    "plt.pie(train['label'].value_counts(),labels =train['label'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x232f2a39198>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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HItm+Gqg8+jOwWmSFDrQzs49R33FplMmugQJdF5RVPocC2CXuPsLM7o/PbgNOd/dM1/hklO1dHGvv7+4eQWgFxMQ9K767NeqTvofGgpqgXuYtMTqToU7R/f6OhPY/RhnjHe7+Ydz7/UAnoIaZjQbOdvfbzOymKAGXIMLTybGvJVGP2VH5OTM7OAq9EMyMzHQGyNLOzB4xszZx7eXRCwIULO2mxd8zMYzKsCTKzmujPu4HiAiW8Bej74CRTJtZMecskZ0SEhZtVKUnO1tLO5dvaTZPejCytNs2yE/9UJBrgghDJ7is6T5CWeUwVAbdjpzVnUsR6X1gT3d/IHd8eJCGJiBlpMzW7RtECgIF/E6RzbYB3nD3lWO9y7IAa2brRSAdBuzl7u/G+TNjL1OAK929b9G+3gbWd/cW7p655Fwbz3JFYO9cf/RbCnKON7j743F8VWAvMxuIMs5dctfYzWXAfgdwr7uX+qyWdq+5+5m5f5MLY664A9Fndvcf3L0LysRnArvHfHI5JOJT9WN2hKZEdkpIWLQx34hPlZCbKiQRufxeL0YM2+dQv3JWAdcKUEyUCswtuQl3fzeYz12A3tFDBdjYpTa1LXCMmW1ahXuvcC2X5GNHFGS7RpkcVCae4pJpvAVVAPL3ODeWdr3R+FIXNBN8atH2dgTedPeKTOIT8ekvwOwITYnslJCwaKMq5eJ5sbTbl0KpsgSVYjsj4tCZ8XMNVEI+v+i6LYD3zayxu/+UO348KkHv7u79okx7g5ktE9d/AwX3x5FjT0Zuqgm8bmaboAw3TzACifwPRIIRh6ISr6Oe6mumRS6ItR82syvc/WozO4XyBKM145yX4tnUQPrG9VDP9iNUUj8oCFzZ88LMrkDBfWVE6voCzexuhUZ4Pov7eyiucQ8S7NgMkbWWinPPi/W6ob7uBCvY/CVUM/oPLqXvgJFlak6zG06eNHU6/QeXpn5sQsIiiqpksvNiaVcfmQZkWebaxHxnXHtzNA/7K3BftpiZrYiysu/zFzFZ4e2GSFEZ/gCOj0xyB+SO84W7/+ruNd29VpCCfgIuCnLTFGCL2NdNiJj0dZCvjkcvAush5nOW3h2IysHvIUJXVsbuB3SOe78czeR+aGaNQ3DieMQoboQIVKBgeH+c837cA0g1qiSey+UU7OlGIULWIfHzlhQs7eoi1axOwFNoTIi4dtaDbUNSe/pLUJGa0+zwy6Rp9H50GP0Hl87hmwkJCX9HVCWTnRdLuwFIPP/juNavFNi5TRAreQpwjLuPBzCzXdB8aAmwLMraMmbIlSjD+ya3v1Eo06wZ+5uAFJ3KEFlufQoZeD3g1SARGfC7u/9kkoFshgKfocA8IM45HQXH5ZDE4RAkjrExcFqs1RIxoX8ys7XRi8NKsc5P7v5UrDUOjeD0RO5E/eP4WWgM6nrEOi4FlaNNespXxH08TmFsaFPUdz0n9nRkHN8HBfch7j4RkcASqhmVWdfNDpOnzaDvgJEpm01IWARRVeLTYP9zlnb3oqDcBJV/z8j1Br9Dv/inIyJQhpmIAVwLEYf2BAgGcfui7+LuE919XZfe70EooH1WdA87A4+7+6txzt3uvmZkkncTer9ByjoFyR02AQblyFXLIYLTMFT+Pja/FgVTgDPj+Icoc+0W+8rv6WDUR20Qz+CkOOdQd8/6r78hFnZ2n2cD+yPS035esLobgF5UZqIxnWxOpHU8vxVMpgn7UwES8Wn+4s8SmRIBKiFh0cQCIT7FZ7MQjEzGAaejjK4YV1Kx4hEAZtYUBcyDKvhOhXZvc0kwmi1ZiSKCkZntAIx190EVbPdEYDuXctUd5Jx+Aj2BfkWjRpWhMuJTCapCbI8y7jPNrHXxyYn4NH/xZ4lMiQCVkLBooqrEpxVzP7eIY8CsalBohOeG+Hgf1KP8AAXb+iiIXYGE8E+J7y2PhCb2RAFuqMn4fCnguyBWdQYeMLPGhFG5mS3n7jdHOfjFWOs2k/FAF3efYmaHI6WlFmZ2GLBvrpx7KypH/xTrbo7cgH6McviOaI72HjS6hJllGWlZbS/UmtZFTj5D0IzuRkAPM9sOlb3rm9kziBC2PnC9mWWi/csUrbU+MCpby93HBsv5ZjSDvLtLMzl7do/GGnUQgeu82O8ySKxiCVRl6I5KyAnVhM3bNubed0ZV2otdwqDGEsa0GYVvJLWnhIRFF1XJZN8HVjezlSMo9ESiDnk1qKMINSjUE8wC0SigZhCMNkIEn91CpOGJKNduhAhO37t7P3dvAtyFAvc3KKN8PGZdL0HZ6n9R6XbL2FN/FMD3jNJtN2BaEKUuRVlhe9QrPtbMWqLAdDKSbRyV2+/6ZrYUGjH6jsILxXNI8H89lIkuEZkv6AVjJrCmh52eu/eOTLV9rDseZei/oJeE42NPD6NAmaEeYh+38YI1X7a3PqgnnceSqIy8FmI0twnS0+PA7vHZhrG/LUmoNvQfXMojg0orDbAN6tXk8j070nf3DjSvXxcjqT0lJCzqmGMm6wVVpwGEqpO7f2xm56Hxl89RdnoN6jGejBjDoAB8R0496XVU0lweeCxGa0pQL/Hp3GWznuW+2kLZCM9OiOBzBBKFuB5lv5uiHuZdseaB7j4uyFB1gGdiBGeZ2G9GMLoTBfgV4l7fNbN+aHRmBhKSyLR+hyHlqFcRuepJNJJzP2IgvxMEo2KcH985Jfc8D0ZjRzNR0P3DzGpHn7UJ8Ly7F/+uvptCH/cmM/vd3QfE+o3j+Q5B0oyNXcpZ2T4nomrC8Ar2lzCfUBnpqSJVpxRUExIWD1SpJ+uh6uQFSzvc/Sz0C/1bL1janQo86+4fx3cmuKzbrqdghH6cu3/p7h3iz5qoHH0/lI2egILHzygYZZle87jege7+EGIrPwf8CwXpH1DGtnVcfxoqWV+LMsB2wG3ufigaybnL3duijDXDzYiw1AK9FGQqSQ0QIau9u2+AsuLsN+X3QDMzG2JmZ0ZAx8zWQTZ9fVCQzp7nY7FOh9jbwByR6Wtg3eK1POz8UOn6iAiwuPsWsdZayFe3BL0kgMadMmb1KigTLodEfJp/SBZ2CQkJxVjorO5QkGgBvBXEqLdRyXd2KEGjNL3iv7uY2ZaRyR6FZkibocDYe14IVpVgrhWnoOrWfFXZQCXErzkRrBLxaT4iWdglJCQUo0pB1sy2MbORZva5mZ2W+6jM6s7MhqGAsYOFS4+Z9YqMbEiQeO5BRgCY2d5xzlNAbTNrFGu2QNnof0zavp8C65jZsqiU+rKZfWxmhyBCUXs0TrMs8D+U/X6Jeq0XAhugEnFDCkpJxxGuQWY2Ja75gZmtgErAb5rZVNTTvN7Mdo573cbMPony925AqZm1Aj6P+3sdzQl3jb1uCHwWa22MBPs7x/0fiUq4Rnm1q9vMbDzK7O+LtTCz28xsKCrFnxx94+zfZxk0c9yayLyDyNUFuDL2ewjqfydUA/oPLmXiH7NIQydSU0LCYo5qtbpz2dPtEQSn/VA5d2QQkq5CIhNTkUpUZk5+CZIOPAllmpeiXuIxqOf4IiI2XQW84u4vo0z1azSuMgkpNQ1HQXcCBYODfyClpNdQ37IWEuefAazj7t/HGl1QcO8HHO2ylJsY97gZCpgNKPSpv4577IKEJj5CPeLu7l4TsaQnAH3cfWCUka8ADnX3VYAT4lmXIAWq/VDw3YGCutaJUV5+As0CHxvn1IpjjpyNMvwWz+GiKMlfT+rJVgsyladiK7sG9WomUlNCwmKOarW6c4lVHGtSg2qEiEQHoABiKMh+iwJExpp1lHGeg9SSaqOSay/UA26IgnsJMRPq7r+Y2eVxfAnU43w69nsW6lW+ijLcA919XO7+3om9E2u9FtlpMfZF/dM34+dz3P3n6Be3Mrng1ECBPbPTeznWnBokpCZx7lWxz5PN7OTYZ2bN9x/UQ20Sz/OWOKeNmT2GgnsNFND7IOLXJoiJvRZwr5nthcrjbyKHnvMRwSoTCUmYj6iM8FSvVkkKsAkJizmq2+qOHClqAjI3/zgISUehALISQUiKJU9AwXNlFEjXc/dRKMC1RllkI2A3d88IPrj7PYiwtK+7/zt3/EYUwDdz9x2LAiyojHqnlzchyM49MDeP2hplyeNQ6TjLMCej8vYMlGHOIiIRZK6pFAQsxqFAOxGpNZ0WYz8T3X3d2NOr8ewyr9n3EdN5AupTZ6Iew4H+kREPRD3dIbHfUXHvM4Bnshelor0l4tM8IhGeEhISKkN1Kz4Bs5KbKiMkxdePQqXRFRFxJwu+3VG5uBkS9L82epHZNZqi/mymNTxHWJHi0xxQgsaP1kfjOA8F8/c7oKW7d0Il7vuK9lWC+qtX54JcCSqzd0OzxbfkWNWVwt0PQvc/AvnRzo5cVSEZrII1E/FpHpEITwkJCZWhKkF2jopPRaSoB5BWcPb5kWi8pqGZvRH93I6oXHwWCrA7oHIsqJy8vZmNjc+7xvGDkIvPYNQrXQFlbhluBmoBH5lZ9/z+UOb9Xp60ZWaPovEfQ3OnS8Xx2mjmd3UzezdXOh6NgvxbiKnbDGk4/5HLjs9Ggbh1rPUg6kN3Bk4IclS21gwULJ9DGfDqcc7XKOPdJIhfZTCzfwIfo/74GUjFaV1k5zcVjUi9b2b7xDXeAP4v9r1aPNuE+YzN2zbGio4lwlNCQgJUv+ITKIv7A2VTl6DMqxRlscvEuMr9QNMIaGNQUNgGqRnl1aNeD4JRd8SifTT20Q71d/eI8643sxq5/f2AmLV7m1k7k+JTB2DzmJMdRYF4dQgiDX2GyEkXx/EnUMA6EpGlfkKSj43jWruifnIt1Psl1ngFiW88QsG67oPY75rxzNoj4laGE+NeMyaymVkv1AvvEGsNcNn5Le0FO79BQKm734cy+q1Qj7wdMBR4loT5iopUngzYbd3mqR+bkJBQ7YpPoGD6rbt/GWVjd/cxZvYIKnl+iALrjyi4HYb6lUei4LR7rHM+cGeM/RjqcWal5ANQ0P2fu880s89RBrwHymJLiAAUe10VZZzXq+JLM+CyWOtMNIazLGI2Lxtl4dGoh3pvXHs/d59h0hS+AGX7o4AxQYhqgbLNT1BQbYfIXCDy1scoK58Rf18tnsEKcV9LmtloFPRfiGfyW9zHUPRiU4wm8e+UkcFqxb/FDqgnO4tJQsK8oSLSkwMvf5L62wkJCdWs+BTfewW428y+QJnscfHRUaiM3BSxYy9x95/d/Y0g/2wHfOnhYuPuY9x968h8j0ZC/lmWuyTypM1EGDJy1lvAPe5e4u7NEMu4uRcs5QbH9T9GLwkgUlJHl+F7C2AsCoqrILZuVirvHPt6BJV894s9/x7HR7u7ufsacc8fuvv/xbnNgZvcvV3cz0AKZLLvULAeDpzn7gPivkYjsYmJqGRfUS1yOsq+M7LVpNjbVGBlKyhnlSERn+YNifSUkJAwOyxIxafZ2eDNCRVa1/2JfZUjEs3h65WpSnUEVnX3x+bTfmexAMxdvyLiFfDnlbMS8WnekEhPCQkJs0NV5mRnsbNzafFCTvEJiUU0BKaa2fXuPjxYxLci9aUSlIntbGZ1UH/yD9R/7YeyxM4x17oZGo1Z2cw6uvsQky1er7juWtqW3YGy2G2BPcysNyJAZeSs84GuJpWl+qhfeo2ZdY3vgUrPDyEFpzviex+a2RhE0FoWZbe/oJndt1H/9KW4rw2AbYN4ZECJmb3h7hsHiel3JOw/nEJJeibwf2b2L5R9TgNuNala3Rg94xLU0+2KxDPqobLvVqh8PDOe/Q2o/N2YwlgRsedJ8e/nce4hJMw3zA+Vp/6DS+k7YCRjxk+mWf26nNK9TerlJiQsQqhWxSfUE20YJdF1geORaMIfaO71RRTItkNerp/EeafEsS9i5hN37xukp9NQ5vmqyyB9OsqQv0fs2uNjn+8hEtRPiKj0WNzvEygY7RXrbYN6pyPj2g+jAArqB7/k7o6YzY6IRy+jgD/c3XeN0nIt1Eee6O4b5x7h+Sib7JA7tm7se714th1jvycg4Y8Ocf/bA5+YWWZVdw96wdgEMYt/QtlpOzRvW8/Mto3n5YjodCZSgupCUnyab5gfKk/ZGqXjJ+NA6fjJ9H50GP0Hl87x3ISEhL8HqlXxCQWldibt3JkoMOzs7m5mV6DMcSgqF9/i7h9GBfQElJ01CvLP2e6ekZx6oiz3fgB3/w7oZ2ZtUKBqjKQEZ8R+M9LWKmhW9eOYL7095llroQz0gli/d9zP6mjutWcc3wa4CL1Y1EDM6Lw9H4hk9GvRsV2YtVQ8IfY6HHnLvhMkqiWRucAWcfw34BkUjH9D5KgPgVaoBz0JaTl3Q4Ih76IsPsOvsde10AvG7iTMF8wPlaeK1pg8bQZ9B4xM2WxCwiKC6lZ86oeCSRMU5E5w97fj1MkokLVEwe+4wiVojLLia1G/87bcZ0ejYPNIfpNByNoSlXWvze8POBQY4u4nxbGZKIAuEfeyj7v/Ep9NQaXvz9y9a05AYnl3vzYIXmsUPzuTs88maByn7PKIoHSYmR2eO34CMg+oFd85MI6fjyzxmqBe8f7uPjOe2UuomtACuNLd78zd4yvoJWBHVB3IbPbquyzwBiIN5sycPr/vRHz6E5gfhKdEmkpIWPRR3YpPlZKb3H1GlGtboL7pWnFOb9TD7IKIPsVqTDsCb0apuAwhJvEICuS/FZ0zC/HI3d+NgNkF2d/VmYt7dSg3GlnZviojMc2VqpWZrYYCewv0UrCFmW2Su/dyqlJWBZu93L0k4tOfwPwgPCXSVELCoo+qKj51zFSdUC+yXNPIwuouFI2OJuzskIpTSzQn+ioiAXU2szZWsL97BZGHLoxzjkYl5HdRr3GTuEZbM3sb2b+VC6Jmtj0SnFiJUFuK4/ea2Ug0e9sliFiY2Sm56z+MAu2GZraimb2MiEWrmdnxucv8YGZnmKzuRhJB1sLOD7nntDezmcE4Btg05nr/hwha3eL44cDVcd7pKKsF9YZ3oKBq1QoF713ivt6M51KSnWMVq0otjUrEb5qs/DYF3o7ebsJ8wPxQeTqlexvq1qxR7lhSikpIWLRQlSA7CJF9Dov/bkZ5Fiuop9g+MtOXUf8VxNRt7gUT8rVRgPwZ6Bbf3yj20T/O+a+7r41ELCYiMwDinN5o5rNsDjeIWQ8AD6IMME/MuhcRod5EQfHQON4P6BzXvzzW/BCRkU5GlnhfouwzW2swKut2QL3k+6DMzm+zuOc90fzukOj3Xo0IXRugHmv2AvA78ERc/2RE5AI5At0bx49HVYABaG52NCJMdUQvEw3jnFlUpdz9VyRqMQ71c19D5eS8FV7Cn8T8UnnauVNzLtq1Pc3r18WA5vXrJmu8hIRFDFUhPq2LAtCtiETzGrCWmXVBlnJPAAeZ7OymIbWkD+PcAcBmQXwqQUSc91DZ864IkMsA37v7HXHOTSbDcYv1BgK4+1gz2w74AgXFDIciv9bOFIhP/0aWds+Y2Z2olFqXAiloY+A0M5uGMu1bPVx4TJZ53VBwn47GXk4G6qDg+BFiSO+Z28MuwPNIs/mBOLYCUsB6DQX40fF3gCeBHSOznIIyWyivarUC8LS7/2Rm/ZAM47BY63dg0BxUpbZGAhhDg0z2qxe5AyX8OcxPlaedOyX5xYSERRlVJT4N9lB8QmMkzd39LM/Z2SFT8KVR0Mx8S+9FgaUJCnBnuFSdPnT3TpGxvoTIUsRaW6Axm2VRwD4999mdKAvNYxxwm7uvHRngqYi9m51zIOp57ofUj3D3u6Mfm5VPz8x9f293b4pIWT8C58ZHrVBw/Dn2tWp+X+7eEwlaZKznTxEzuQXqLf9Bofc6CgVwQ1n5l3FOXtXqNxRAs/71EXFeU5SR9vPZq0q1BtzMBsS/yRZUgER8mnskwlJCQkJVscCITwBW0NZ9uGitM4IUdC8F4f55wfXAa+7+etHxuSFRzZXiks3ezi8TkOiIZBQzkYoK18rg7t1RkK3NrEGzmNyVrO6qCYmwlJCQUFVUpVxcRnxC5eLPEYmpDDnFpxkoi+yARPv3RcL5H6Ce5ReorJuNxWwbx19ERJ2MyJMxPxqisu3ZJoeeEajkPMPMVnP3I2N/Lc3sZlTmrY9Kt9nezka94K5mdo+7D4zja6PgOznKs13cfYqZvRp7HA+cFepNY+NZ7Yj6rz+iF5RG8XeA/WMf17r7sagX2gHpM89ArOHsBWM86umui0q/9XL7XRsJZ9TI9hXXehgF5hlIM3on4AWTNd/diGw2zMwecfevKZSn6yGFqjeQQtWLJPxpzA+VpzmtnxSgEhIWHVQ38akp0DTKnzshEtDI3HmnUAi4Gf7j7h1jrU+LPv8CuBG4IgIsSByiCyrHroUywxsAzOxQFMinkSP9xMjLfShwtkHBeVpkpm2Ax929eexjbJz2DHBflLhfQ73frI+7BHqpeCW31zGoHLwJymY3QbPBoLGdX9x9NdRnrZvb1z3oZWaDbF+ImXy7y5avMwrg2b/dIfH5m8iWL7PmG4Bmdq9CZfJVSIpP84T5ofJUlfWTAlRCwqKD6iY+jUU+sR+jgDMalToxsyYoWGyElKIy9An1ppmoL/qPON4YBcCTgJlmdgLQzt1/M7PpiD27HQpGb8Y5N6LM+mfUFz4UEam2Rj3PZ919ImIxY2YbI5ZuFysYrJ8eghanIZWoj+I5fBnzssS6U1AGmrnzfGlmZ8TzmoZ6sFm5+jiUQX+ISFRLRoDfGo3jTMqJYGRB/LTIypdA2fvQ+HinWPt+1K++1szMZXX3KupFzwQ+9lkVqhLmAvND5Wlu108KUAkJf29UN/FpICLrdEA6vI1R+RjUn9wTBeIyuPtuoVJ0LAoMb8RHPyJW8RcowOwdAbY+CnBPolJyRytYunUFXnT3VWIvt8bx1rFOAzP7wMz+Hdd+A5XCp8T3HiGMzt19qrvvG3t7kWARRwDcB/VXy8HdbwxSUiZC8UT892dgsyBr7YjKxw1jX98Dvxbt6wd37xJZ9MbxjF/I/fscEdeaHs+gYfSV10eEretR2XoWJOJT1VHdhKdEqEpIWPQwz8QnM9sm+rUnArdQnvh0Oxpj+R31JGeizLYjkkbsgUqZq5r0d7M1L0Tl2bxE4Xdxzqeo9Pk/k0pUZum2S9zPhsCDVlA9+jgy6S7AhSZlpxJURl4dlXL3yJGCjkQ9zBpxL5lbD2Z2sZl9i14ivorDR6O+59NoBGebCHCY2YFm9iN60SgmK51uZh9FZrxkHCtBLjsrxPX/Y2b7xVormdlLsdZMyo8xfWMFcY0V4tg5KBC/Fvd0jEk5qhwS8anqqG7CUyJUJSQseqiq4tOKuZ8zG7kKHXpQlrkzQGRWu7p7XXdfFpVC90L9xk2QYP1SKKA8GYERFGAnozIosdYfKJD+EszjL1AQzSzdWkcftwfK9uojwlPv3DU2RWXf0ciybgtUrv0fIgWBCETDo4/cGwn21zKpSm2Fys/tkLrSMnEvTdHIUd14Vk/lntePwJ3R480y6T9QWbkjeiloiILmaBSwe7qceF6lYHJ/KQrCd6K52oty/z5/xL13jueWiVD0RuXvOqhcfg8JfxrzQ+VpdkgKUAkJix6qEmTfB1Y3s5Vj5KYnhbJnV8Q2ruHuU1EJ9d9IhSgTzc/ccLLe6gR3vwGN5xyNAuKnSEiic3xnaUTSKRPVCYGKnZCIxSooqK0bHz9JyC8ictV01HNtj8qvnRDxaSDwFiIFNadQqt4QGB7Eo7rA0jGCs22sMx0F2JWBHsHe/RDYxt17ufuK7t4K+Ffcy7Ox7q7IBOCEomc6CspeQrZDBgzbxL5qA41jL2tQIH51QwH+BEQu2ymOP4H64JCz5nP3TeLcXYErUcDOLPwS5hLzS+VpdkgKUAkJix7mSHzygp3dAMLOzmUXdx4KIN8CxwbxKcsYu8fpTYABJjPyZVDvMcvMhqKs8y0UJNZFWeB7KJDfTyGIgrLQzVBf9Q9UAr0SZYGnAv3NbI24p6MigJWa2aUoqNVC/dnnoUzZ6X00w/qkuz9tsprbAZWRD0NBfjd3n2lmm8X99YuMuxxb12QgvysqjV8Takw7ogA90cz+AC50975xD3eZ2Reoh1oCrBhkpb4oGHp8dlis1Qhl2x+gF4ylzawhEri4xqRRnMlCZjgUVQVqogy3Jwl/CvNT5Wl2SApQCQmLFqrUk/Wws3P3VV2Wcrj7WRQkD48PBaX/AP3d/eM4/rW7t3H3Fdy9Hsr08v3a0Yhc9AkKtjPivAPd/caiPTyCsubu7r6Ouz+Z++wbl4JUHZS9HmZmdcysAcr4VkZ9zz/MbN84557Y8xhC1SmYxhei3nItVBa+2syWcbnpnIt+t36PtJZn5PZwEBKreAIZwo9GgXEpd6+LXgQyQ/WngStQQB6HWMfZWmsCG7l7TTSOc3ms1Tyekcc1SoEZLmu+FePe10aOQpka1YnAdu6+DCo3X178b5uIT1VDIiUlJCT8GVQpyGbkJjP73MxOy31U3K/tjgg2nXPn9o7zRiJW7c7x0eeoBAsKIPWBT82so5m9EySeumbWNdbphkZ4XgqSzzkooxsXn9cws8FAX1RWXQsFls6oLLws8CgF95pucY1mwOO5ezgXBeaPUFb7FdA2yFo7xncaoAz606K1Poz1d4vvDQIGxmeHEZm5me2BGMlZz9ji3pvFM7nVzIaifvGGZrY0ykhXie/2ApZ19/FmdhISpfgQvRy8D3Qys06I0X2TFbSjZ3HhScSnqiGRkhISEv4M5hhkKyI3WcGZJt+vXQ4F0A9z57ZDSkhrop7jHUS/FgWL7YOw0xuY7u7DkY7xuXF8KjldY5TxvhSfZX93M1sZZW0jUE+1LWIIv4KyzsysfEtgRIz9XI/K1WOQoAPBVl4Blao7oPJvO9TbvARllR1RFr4J8HysdUustRYShfgkrlcD2DzOuZCC285wxFB+DTGm10YqVbuhDHo3NB98CTDC3X9HwXedWGsaBdP6z4ANY7zn2XjOw2MPv6MxqfVQGTljRCdUEf0Hl9Lx3OcprSBjTaSkhISEOaEqYhRdgc8zcQQzewBlesOL+rVN0C/+lYAjIitbE2WvH6DAUItCybIGykqnoYx4vzjuwFFmdgsiAXWNrPUV1P9taPK1/ZlCj3FHNLLyPZrFPcDlqnN3jK2cjkqyA9FIziGx1lsoqL5kZs/EfTyPxn0Gxz295HLCMeDy2EtN4NW4/16Ivfs0enEYSkGjeGngjejH/oys8kDM6A8oWNbtG2u1RQIaj6AxnWWRZCKI+HRREKKWBo6I4+OA181sJmImfx8vK5jZYbGWoX7y+SRUGf0Hl3LKw0OZNtNn+axBvZqcveOaqX+akJAwW1RVjOLb3M9ZfxBQvxYFuxfd/YA4fFMIVTQHbnD3NSMD609hvnMaYu5OAh5y92/i+AlotMXRbOwa7n5OfLYeEmz4DDgop4qUkaIOBV539/65/Z2Ngvgm7r5fjAK1RuXqz1FQPM3dD0Ul4nXRKNJ6KIP9KZY6DgW+pVEwOyqOr456pOMQIWuAF0wFfon7mw7c7+6fxJ6muHs7VAk4yN2HxPeHxjPrhKoChl5GcPd+7r46qgbcGPeBu7/lkrTsgF4S7qKAgfEcVwFOdvd3SKgy+g4YWWGAhfmn8pSQkLBooyqZ7GyRE304cC5P3djdS03yii+Y2Sfu/hoKXie6+yNmtidiz25FZH7uPsHkK9sflap3AMa6+yDLCVrMASUomG6Jystvm9k77j7CzC5G2exEJKCREZIq21dla306m3usDLejsZ2BqB9bRgbLoSeFrL8MQejqjF42AHD3b4G1o6rQ38z6ufsPRecdTvjZtmzZcjZbW/wwO1JTIjwlJCRUBVUVo+iYEZ8Q6SavWL406kW+YmZj0S/5Z4P89AtwrplNMLNryQlZAD+anHPeQNnbIXH8QDQSNBgxkTcEiOxwFTN7G5GbVjKz5mgu9lAzm4oUjrY3sxEAZrapmWVl2Yy0BMrGu6L+6f9QGbtDfPY1yiCXQcSnX+L4YcA5QWI6DkkWgrLt2sA7SG7xU6BDzAjfbGafoLnW3+OamNlJZjYcBcXrzWyluMfpKMi2QhlsfQrkqtuCPLYqYhDnVaXGo7GgpcgFYDObEft9BpXFs1niMiTi06zoP7iUjfq8RMU5rJAITwkJCVXBPLvwuPuv7t4Ijc5kZJuTXJZyj6Ny5WkoGK8OvBfzqOci1m8nFEgybd0ZSCu5E5qDrQEQAfUeNB97UJz7vbuf7O613L0WIgv9SqFfOQoF7YlF9/Q4Kv12RkH8dwqkpJvRi0QP1HvNmC2jgH9G2fs5ChlmbaSq1CnO2SKeQz3gOpdzzkaI3FQnzhkc1x6IAvMlcY/1gGtQoGxEgQwGInY9BvSJvWQ+uy3RS0F7d1/LQ1UqZmsnx343R3rMeQekhAqQd8KpDDVrWCI8JSQkVAnzy4UHRKq5GCk5AeDuA0Ok4SwUsPZx9xkmAf+T0S/9XZCF3ENx2mvAniYt4RIKTN0zUED5LxJW2NPdi0upLVDQy9xueiAFqnrAZWa2kbsfGmXhGXFfM4FbvWCQ3hRlfr8hecRMFeoQ4KogHi1J4UWjIVJTytb6FpWNl0FEpYviPj5CspJQyFAbo5eT7GWnCSq914k9l41CucwQ9kQKUcdTUMPaK57tw+JmMcrde6CMuG6MAhlwqbsPI2G2qMxpJ0MiPCUkJMwNqhJkMxeeQwFMgvXruYzJiWPrIEGEp6M8mWWFuPuFZlYKdHb3TG7wZxRwXkCs2Y5mtnz0C49EPdGGKJhls7SfIvnEJig4bYx6lnmsgrJHj2tfjcQk7gSecvd+ue/WoEBK+jp3vDvq99ZEWe76sdYbZnY7stqrRaEHPRRl0GujmeHB8SzeI0rQMebzAWGY7u6PoayUKKN/H8e/BtpEb/lfOTJYhlfjz3AKyk59kY7xEvGMToy1XjAzp2CzN5YKkHqy5VFZr9WAr/ps/9duJiEh4W+P+eHCkxGfTp7Td3PInHPeCiWlt5FwBIjZe6e7t0BZ291xjRIUWHvFf3exgnNOhkyOsSpYyd07I1GIK21WlaQWiMlbppLk7te57P6KnYZGo9LvlRSRlSLzvR+4OseGzj7LyEp9q7LhUJVqhl5i9orDTwKtYk72Bcqziyu7x/yaqSebQxKdSEhImJ+oVuKTmXWNzPY8oKeZ7RLnjEPZ1fUmq7eHKbjgHAI8ZGYno4BVD/UnV0Jzoy8jstIH2TlmdruZ/Qys6u6Dso2Z2TmRRfcA+gYrGZPRwQVmNgxllJ8glaTGqH96q0lBaXuCLGRme5jZxzGP+hkF5arN0QhRDfTi0JICWWlvlEF2QRZ4jXJ7uxYxlOsT86tmVtPM7kKBezMz6537/vHxrD6MZ7cbgLuPAw4PgtVxsX9MSllZZeExNI7UiYRK0X9wKRP/mD7L8SQ6kZCQ8GdR3cSnj1CmdhaaJb3JzEqinPsacGYssyUFsf1RwB7A1kiNqQ6yi3sK9TvXQ+pJ++bOuRORmX6tYP9XxLVPiZleUDBawmVntxcqWY9AfdjGwKGRGU6mkJX+jhSgXkOZdKZcNQH1h9sjIYnV3T1z9LkVMZiboOB4LJSJRBwMdApiVJbF74F6ygejvvIRZtbKpER1DGInd0BiIN/HWrvFzx1Qdp31XUcjJaiOKJPdlER8qhQZ4Wn85GnljjeoVzM54SQkJPxpVDfxaZKZfY1IQLVRwFwDBYKDgYeQrOB+iDEMKju/hrLdxogs5cjNpzEScDAUJJ+O67xmZvcgl588miFZwZrAjmZ2rssUYAOkJDUUvWh8ifq/M1Ewvcuk0tQYuDbW2hb1h1dCozKZ8MZ3sbeZKMOfbGa1EYGqHpJ4/ABluVmQvwAJV9yXkZVQtu2ov7tlXKMEvaQsgbL4d+Pex6M52mytusi96Gf08gEaA7op9lUbMazLeuUJ5VEZ4SmJTiQkJMwLqqr4NNjDhQeN0TR397OyAJsnPiEBh/wv871QIHJg94zhGqSevZBk45bunukLrw7c6+5tUCb7SraQF5xzbgceLNrnphTUpDKMQQHpC5TpbhzHB6C51nVRcGsW+5+GytXNUPb5OXpxyDsNDQSO9FmdhtZAmexAd/8jSEx7oPGe5VHWnb1IfAdcjQLtrxTkDvuhvioo4B8TZK1h8b1uKJudFPsj1rgLjegsgcr3uPtbSAEr638fGHO45WDJhQdILjsJCQnVg2onPrn7uxGcuiARhToVfS/Wqod0hs+azXc2R4Hw1Cps7wYk3tARBbbL4niFZCWTUftRqHfZDGXwvakCzGxNFJCPiJ9nt1YJssVbHzgF9aANBdAZ8f2VgZPNbBV3HxFrP49mdIdQKGNXtlaVnn0iPgmJ8JSQkFAdqCrxKW9nl1dtAhGANgY+M7Nf0C/7Jyxndxdoh37Z7wFgZv9AzNjVzGyQmW2BAuLKwOeh4LQS8K2F64+ZPYeCDMCgIPVkOArJLI40s8w0/lfEXB4c190BypSVnkLZ4kqx/ykoGBvwf6jnugUq3RK90cmofHuvmZX53ZpUqAbHeo+YZBQ7oipA//hsU9RnBo0vbYxK0/vHeY1Q77Q9Cv4vo+y/S5yzIlJtWgb1hmvOYS3M7EIz+xaV2DP7v4QKsHnbxljRsUR4SkhImFdUJcjm7exqoTGZrExcA42ftEY9zVEoY+sRQhQrm1mJyQ/132hmc0ys+xPKSD9H/c27o5TcDAWXZqjveCsSp2iJ+rebR2n2EeQPm1nq7RhrbYNYyzWQ7+sWLvH8m4AaZrZ+ZMw3Iqb0KShQ9UQvD+sipaTVUNm7ae5ZfIECYC93PzKuXR8FxjPcfXV37+juY2Ot+nH9jrFW9rLyOMpG/xXfqRXPYxTwYey3azyDleKcJRGJqweSejxjdmuZ7P+eiXWMgv1fQhH6Dy7lkUGl5WQUDdht3eapH5uQkDBPmCPxycvb2dUAbnf3j83sPJQ9FdvgHZY7fWMkqdgEZZWfUmAA/xv1GBuh7HTZIAzNRL/jlozvLY16kmchgYrroxraDlnpgeZQWyAC0OuoD9sVOBqNHzliJpeiAN4EBa9nUOB8FJjh7mPMbDTQLUZ4RgGNo/y6NZJYXJ0YjXH37ogxXBfpJ/eK/Wwda50DvGay85sW1wIF+NsRG7gW0NPd3cyuA+4wmawbKmlnBKc9EXFsP9SrHT+HtbJnPy3O6+Wy/0soQkWkJwde/mTx7VEnJCTMH1SpJ+vuz2TEJ3e/MI6dhUzAi23wnonxHdz9bhQUXovM8Kfcmnu7e1N3r4ns7d4JwtA0VPodhlixqwC3ueQQG0RWeBzK+LIxmteBo929ZohIvIvIWfuhsu1MYn43+pRfozLwMijD2xFpAoN+v27s7mu7+w4oYDdE/dCpFAzRL4j7uCCun5GuHkEjR7j7jYidvCQK7EfF8anuvi8KjA+5+0txfIK77xHPsSnKUDOVqjvQSJEBvcyswRzWutsLFoOTPWf/l0ciPiXSU0JCQvVhnolPs8OcSFHxnbkhDGXYmyoqO7n7jAg0LdDYTtaXrFTZqRJ8B7R0GRechMZvlonPesWc7Cbxp8wJxytWiZrTnrujIFsb9YWhchLXPCERnxLpKSEhofpQpSBrZttkik9mdlruo2JSVHfgmBzpaUdUMv405k43JEhRZvaKmX2BxC6y+VRQT7UTUil6Of5sGPu4JEqphwFrZixalH1eGvu7miBnmVlHM3snCFL/Q+XqbcxsldjrzbHeTGBDk+VcS+ClUHc6Gs2njosse5yZPYFGZr4AWpvZcsCdZvYZKjv3R8G8gZk9ZmYfmtl7KDPf2czqmNl7MaN7IWF/F/d3p5l9Fft9BxGmdoqs9cb4+R2kX5zZ5tU3s35Iv3gfM9sgjp8f1x4C1DF5yiYUIak8JSQkVCfmGGSDQHQdEmNoB+ydsX0pT4paDjGNP8ydPgplf7UQocgJUhTqB89AYhOrB1kIJFa/BCrnngecDYwwsw2RZOApSEVpDQoG5d1QkF4TCfWvjcQZrkDuMx1ReXcvVOrdB5Vd94hzz43j3yEVqpeQstS5SF/Zzayxme2OWLo1UW/2S5Rlv+3uq6MXguOQ0tXpyBFnbcT6vQcxlv+gQMY6C2hpZpk3bQlwYey3MwUVrdNR7ztb61oKqltXoZ52b+ABCjPKfaPk3TGec6VjUYsrkspTQkJCdaMqik9dmZXctBMwvIgU1QT1I1dCcoDNvKAGBfAxCp7Z6ElzNJJylpllAWBr1IO9GKk+TUPBdHPEYK6DSsX9ECP5BzNrGmveifqldZF4xoxgQ/eN9RugoPeUmbVH2eAj8QxqINnFqWZ2E3A3ellYBo3zZHu7mXhxQFKKP5vZTkjlaWdU3m0C3IIY2JMiU54W++/u7g5MsIIS1rIou+8WeznNzI6LZ/UyymAfB0pMWssez243M1sWjQZtFmvVAnqY2dYuacdL0AtFTWBfM/ve3c+p+J958UNSeUpISKhuVFXxqZjcVPYbyKUH3BN40d0zqcGbigIsSND+VXd/O37+Bo3cgIJdp8hmhwKTYkznHOJFIM57GZWgLwAGhEhDc2C0u18Yvc+9UPkXpHiUjfIsEeeCMsE6iNm8IlKiypi/jRGLeAWkwfxOHO+MJAu3Bb5w9yfjeBN37xBZZlvAXD63Q2Nfa6LsvCZhHh/VgfEoKF7q7k1c5uxTKIhMvIDs7qbHWsOj73soCua10Ezxj0gV6xuUybbxgtH7NBSUhwMrVxRgF2fiUyI8JSQkVDf+Equ7YnJToDKy0L+QA81glKGVIjWm1VBW2wIF1i3MbJM5bO8o4ER3XxERnW6L490Rc7cZIhJdm5GY3P3bCJirAQeY2fJm1hE5/Dw2u4tFlpqNW/YB6kdP9J+onzojvlcZGas3CtRdkIrTqXNYqwQ5Ed0QhKyJaGwn288Zce/3EuYEFex5sSU+JcJTQkJCdaMq5eIyqzuUFX6OSq0Z8lZ39VAm+KyZbYtGfJ5EKlCP5rJFgNui1FuC7ODWA/6LgmE7NBKzMsoMx5uca2pSkBR8Fwn9342C8rA4vgwF/d/DUVk1c/tpHf89CGXkn8U5v6Lg9l6IS9wa97QCEsz4BehiUnwqQaIWb7j7xhRK1i2QutQPAO7+m5m9gohf6yOhiKzkfiHqrTYALkFkr4/Q7PDmsceGqNR7nrv/BhwULwLD4x6/RAYE04H/xt6WJAQnzOxI5NwzA2W09VF/OyGwedvG3PvOqHIiFBnhqf/gUvoOGMmY8ZNpVr8up3Rvk0rICQkJc43qtrrLepQ3E9ZsUGZkfmSQfzoiElHt+PhdoH1kk40oqBTNQEF1bSSPuDsw0t2/Q+XhfyFW8kjUwyT28kRkjSdTIAX9jvrKa6IS8FrA12bWApWSn4trjQVecfcbUDA/N/Y6FpGvQL3XA1Gm/jkig2FmrVBQW49CebqGyUnoZQqs4n/EcwO4JPbaKdYaEWvVj/7y+eilZ4y7/+bu36MS8+lx3r3oJQQ0d9w+jn9AGAckCLNTeQLo/egwSsdPxoHS8ZPp/egw+g8urWiphISEhEpRrVZ3yM6uKbAdUDdUiLZGZc1HYia2Bspa/5c7b7hJpekbCgIWdVAWNwiVZMfGH5At3pWoT/ls/AFl0TsGM3kKymxBWeC6SCLRYt1V0QvBnuiF4TjUL30rztkJMZFro37qzqic2wdlsPVRVtk/vr8/Kvm+jUhf/VDG+jGSoqyBSFovuPtTcc69EYQNOfccHMfXQP3WBmgM6fXcs/ocON/Mzo77ypx+zjSzNugFZBoF790EZq/y9PInP87y2eRpM+g7YGTKZhMSEuYK1Wp15+4XuPuSaHzkQQ9dX3ef6O7ropGZZmj28+E4p1+M9LRGgfHp2Ecz4Bp3X8Pd2wEvUiBg/YHUkMYjJagsQRmFAp+h4PZlHF8GODcyvbVQ9tkcBfUhFHqeXc0sk3dc3t2/c6lFrYGCICj4f4dKy8+jER+QHd2lLhu8XePazd39Q3fvFJn6ZHc/L3vQ7r5F9Km3j31n/rPvotJ7O5QV/5H795kQe52JXkDGx1q7IQGLTG0q3w8vw+JKfJod6SkRohISEuYX/hLiU2WoRNkoW/cMFGjureDUYmzs7usg5u8xZrZpHJ9blaTZEoly+84TnK4ETnX3mcXfmwf0BPoFSxmkwfyMu4+u4LvzpDa1uBKfZkd6SoSohISE+YW/0upuVTPz/HEz6436uxuSY7+a2WUoKKxNgWFbChxvZkNN4v3bAz/n1v4AOfyMJPqd7v4DKkV/AOyC+rjZWpuZ2Qdm9hEq436PyFClwH9CkalH/AERnK4LJajPYp+GRnseiLnXfePvO+efm5mdjIQ1foufu5nZr6iEPiQ3J5y5+pyG5l1HhILTBsDZJvu/W4GjzeyHOKWdmQ1Cnrit4z6ztZ6L+zgD6BmjQ4s95qTydEr3NtStWaPCzxISEhLmBtVqdZdbow4KmO/GeUtFttkzjg9CsoY1zGw7NKayCSrLZgpTT8Q6XVF/tA6wXpRzxyHy0UOInPVRXKcpKsl2RAIRWfn1SSRmsR+ana0HtA0ikQPfBynrYSSdWAv1b7eP/d6OysGbufvK7t4K6RmPQqSk/kigY+sYz9kOlXRfyj2T17O95UvGSEd5BtAq7mWEu/dy9yVdylmHornZ64JANj3uYR3Ur+0W9746EszogIL2JMLLd3FGVVSedu7UnIt2bU/z+nUxoHn9ukkBKiEh4U+huq3uiAxvBRS8GqGxnG+B+2K99xDbthYKoDejIHNrLDEF2MndL4r1h6PA8gbqQy4fa4GUmJ5198zY/RKULX6IWMpZX/KH2PsTsdaFyAj91jjW08zWQ9ntd/Gd/6JgNhIF07GxDma2FAqyQxGhCpca1Pmo3/wz6pWOj+sfgWQja5us9W5193NCwakbEvNwlIVn7j55rIYMEmoDl6LRphqIPNbZZBl4LLCVyWZvPOo1ewVrLVaoqspTFmwTEhIS5gXVanUX2BV42t3rIqbtV1HG7Y+kDNdy93/GOs2R7d29keF1RESf5nHNCxG7uT7KnK9x9y9diksd0OjOo7l974cCy1RE/lkvPvoJEYb2cvc2qKyblcTPQCSpxqiM/U93n+nuA9ALQCMUSJ9xKU6BmNWXoRnf33L3Pg7Z9LWkQIgCGchPQqYBwwjSF3oB+QxYwcwGm9mtOeJVhi/R6NJnGYHMpVG8JsqOP3CZGRwfx35AGfF3FGzzyrC4EZ8SqSkhIeGvxAK3uptbuPtBiGk8AkkozgkruXtnJOxwpZmtGlliT+AKk0PO7xTkDCtUg6pMcaoyNagQ5jidioX5P4h9dQCuoTD2UxXiVYU2f5Woas2WXBafL1bEp0RqSkhI+Csxr0G2BeqZZhZ4xaSo4yhY3U1BBJ4nTK4z6yN7uqEmcfwWKMM8EZVrPzazPhRs61qa2csmucXBqPy7m5nVMrM7QvGpBxKYAMDM9gaei3Lx9SiT7mRm56CsbmlUpp6M+pkgXeQ9kEDEqihbb4teFjqj8u8tSFVqA9QL3iZISSNRD/cVpLu8LvBjqDG1AD4wsy1R6f3d2NfSQE0za4Sy9pnATWY2BgXcdcxsWTN7MkhMh5Er+wa56VfU797fQ1XLzI6NfxcHlkJZ/k6z/+dc9LF528ZY0bFEakpISKgu/OkgG6Snf6KS6HYoUzyAIEUFbnf3kiDs7Ikysx4o+HyLSpnbo2xudUQuOgv1O3dBAfqwWPM/wMuR4fVEZd1P4nNijOV5YH8zWyJIQVcj55u1URl2awqiDLdEOXo9lOHdGASrRoh8tA0q67ZBpd31URm5I8o4d0fZ9HZAh7jH84GJ7t4NCWIsE8dPQrOt68R9n4RUprZBNoI1kGft+ihY7oVeCH6K/R4T/z0V9bD/E2Qs0MvDz8An7p6pUIGy5b3R7G+NeM6fsBhjdipPqf+akJBQHZiXTLYrUhs6EniKEHPISFFm1sOluZthSQoZWDvUO30I6SCvAlzl7hPc/UVE2nkKBaKh7v5xnHtwZKxPoj7rebHWV0Eg2hGRnz5Hvz9LkAH7UPQS8JQXHGq6mdkIxIZ+0t1fQpnezSjwPoH0g29B2sV/IMLTMBQcR7iceByJW4Cy0ukA7v6yu0+K4++gQIe7f4peMj5CgbgucHJOQOOfqBy8EyI0/V9cY2n0cvE0CqrZDMraiFi2eowDDTGzJshU/npU9n4FvbjcyGKM2ak8JSQkJFQH5iXINge+zUhRiLD0GYgU5QU1qGPM7AvE9F0/SFHZDOrFwFZI9q9MaMFln9cVBZOsn3sO6p02QHKF20QQH4pkEFuhoDwREaqmoSy3GSI9fUxBppD4/jSkQXxX7p6Gu/vWkRk/BHzk7qWIxbs7ynSfCEUl0EjNM7kgv2oFz+oQ4AJ3/ynu79ogJR2GAncZIcndhyCBi/7uvoO7/4LKyGugfnFv4PhM/MLdL0AiHK9nZDGXqtYP7t4FzQ5v4u7/dNnmlcPiRHxKpKeEhIS/GtVKfIJKVYduR0F1IAoob1EgHmUGAvcDV2fjQaj0eae7t0Al2ruDWFXhWiZd5KOQ2H4zlLH2jrXmSgnKzBqgzHLlWGtJM9s3Pj4R2C72dQfq3ebP3Rf1cvsWHW+KTAcO8lnVoorJTZVa880rFifiUyI9JSQk/NWYlyBbZoFnZp8DvShPesLMjjSzYSYf1KPROA+o55hZuq2EMrElzKyemT2NepFdURk0w7+Aw2Ktvqh82iiys7GI3NMWZaifomC0BuptDkNZ6YZQpgR1NOpz7kHBzL0ucHHseRAqxZaibLs1mkMdgsrDG5nE/DcEbjezmbHehnHvXeO53Bp73S73XL5Bozi1UJaaHe9rUpP6B+ot14+PTkDOQ4ORwf3vca+ZQtS5RPnbpBCFmT0Yz6oZIlwNYTHGnFSeEhISEqoD8xJkZ2uBF8jbrb2MmLMAjwEbxfHrgCnuns23/ozE/5uhQLZtHP8I6BPn3IfmWH80mRPsg1jFF6BscyQKjrVRHxMUuDLruN1QZtoBZbVvxHeeQcINndH4TSdENBqFAtsGqCS9KlKc+iWucRKa312Pgp1eDdQXbo+C9E1mVhKEpSaofLxyjBdleAG4Auk1f0Ih8x4B3B/3vm/c6zfx2VWxx1fifjJzhr3i+2NQD7tsfnhxQ1VUnhISEhKqA1WxuqsMVbHAO8jMsp5rzfg+KMgMiOyvDgV1p+VQEPkEsWyboyD5LMrmbjGzE4FlCbcdM9sLZbxDUGB9D+jq7m+bTAZuRuMzHdG4Dag8PBNJRn5NzJa6+yNm1paCqtRkoMTd3zWzhxBjd3rc7/9calgHo37tyihjzaQLz0fZ9cMoY83Ku3vGPfc0s+y7B7r7EHd/3sxOR/Z5dSloLZ8N3Bmkr1pI2GJcKETthV4AlkLZ8SHxbI9DJvArIHLaI7P8Cy4mqKrKU0JCQsL8xrwSnwb7bCzw3P14xHBdGgWZg+P41y4LuDWIudA4PtrdLY53Q8Hj3PhsOMpgl0JBrlfsY0ngRJcF3laIWZwpRN2IssjP3X1Hdx8X54xH/dDJKGC3yG7K3S+M+zkDeNfd/4jjZ6PssTl6oXggjj8WJKmBwBFZDzn20gO9XLQE9nb36e5+Dwrs01Af+vogO2XX7+aShTyY8MV19zHAmejfqzkKytNRYP8QMY4/QWbzb8Q5V0efeIv4d9qzon/ExYH4lAhPCQkJCwoLivgEgEkfeJK7f1R0vCLiU5Ws26qIEpQ1rw+cAjxkZmUaBX9WPakY7v5usIi7AL3NrE58VJk1X3b9WWz+KlnrTytE5dZd5IlPifCUkJCwoFClIGtm22QEp1B2gvJWbkciMtKuZvZGiDrkz2+JyrZ75o59jTKwhmaW1zoGafCuh4hOl8T3W5nZ5CDw/JtCrzW/jyfiGqXx84Ooz7qqmX2dI//MQAFucOyrFQX3mqtR6bmph3pSHK8d632ESs/7xvGusW5n4D4zy1vNHW+y0uuH9JbXio82NLOPkQHBu4Q1X5zzMHqBWBOJZ5Q9rygX349IT2shVvVoZC7/Ccqc9yna16HAIfl9LW5IKk8JCQkLCnMMsqHsdB0KSu0oWM+VWeChvuMPiFx0CTHGYrJbI34egljDeUxDWV3eY/ZuJE7RLDK3S+OjVsAXQeY5EzGGoeCasyfSFqiN+rK4+16I1fsF6klm5J8bgPtirf+goPtKMHW3RRls2ViNyWXnRER0ahv3mgXMj1CAHYiEJDKCU3dECuuK2MsraSlbEvVOd0Wl3fUoWPMdHt9dDY3tXG+y/1s5rrU5ImxNAL52WfP9hrLVDmjeN3Mg+giRt95Cjj83RYVgsUJSeUpISFiQqMov3a7Mame3k8t6rjILvKzueGxkUHVQKfOB3Lp1gNH5crCZtUAZ4ijgnajgXouIUfsDq0V29guScCSu2R8FmO+Rn+uMWO9+lKE2QsHxzLjU7Wjs5qP47IEgUR2LWM3HEYbqKJs0xPT9Eb0svIICu6FgeE3c8z0UCE47oP7pWyhg90NBclxcHxRM7/KCNd9FyNrv6fg5s/9bLfb1MnoxOToTtoh7Xgm99HwJHBTPZVK8eNwfz3qxtLlLKk8JCQkLElUpFzdnVju7jFhUZoEHjDcpO3WlUMo9A5VuV0FB8IfcOpPRbOygyOBw99FIwelOJGP4a/wMklCcgX5HLoF6qhmWQWXSzclZyrn73u7eFGVyQ9z9ojg+1d33dfdMIapPHL/AZY7ekYKh+tiYq/0W6Oay5js29tbQ5b6zGyJc1QV6BinpehSUt0QazKsAK3p5a773KQRcUEA81gs2f88jMtnd8Rynxf03yZ3TBL28TEZqWKvlPrsBZdfDgCN9MVR8SqSnhISEBYn5RnyqhJR0DnCFu0+o4JTKyD+VkZK+A1oGweck1P9cxiqxmitCZfZwFRKv5hYVkZJcXrMXo0D5HMqAZ50jqTrm9nnNjniV3/siTXxKpKeEhIQFiaqUi8uIRYEyOzsz2waJIdQAbnX3PiiruiG+uxNwnJndgwL6BJPl3XvAzRELDAkodEWjMaORv+oHcZ2ZqKS7FPCAmTVEQhhfIhWmrYHuZvZHrGUmq7kLUYZaC5WKe8We66Ee8qqoxFsWYM1sJZRZNkbl4hbuPjqOt0RmA9NQj3pZNKv6HGIclyDC1gQ0L7wX6q9ORSXiScAXZtYq7nckyjp7o0wYRHba18xudPelKNj8rYTK240piHV0jX55SwokrraolN7IzNqjfnateF4TUR+5mGS2yCKpPCUkJCxoVCWTLSM4mdSKeiJP2DwhqgcFQtT2hFEAsK6713LZvT0MfO/u16IA2S1KorugIJO54/RH2fAINANbCxGmrgSudPfV0HhLh1inFXCCu9dGJCd3Wc39hILcKSiLvCJ3T5ciEtcUoJ4VVKUuBf7rssabinqkoCz6TOAlRFQ6F3jL3T3WXxcFsJUQ8/hrpN60eaz1HRLCuC/WywhcA3PXADGdsxeYlZH933ux94dirUtQ3/ojd38ZMa3vQyNFU1C2PA71h3tFSXw8Uq/6msUESeUpISFhYcAcM1mXqlFFBKfbgV/d/UszuwopC/0PZWgZKSlvdVcz9/dlgMcik62L+q/Px2cDkMLRFFQGzUZPNkfEp1PjnC/d/WeTKXlFVnODAczs/4DbgP8zs9ou+7mXTUbx36IRmkyMoh3wk5ldiFjKvczsC3c/x8xuQgIWH8b1/i/O6Yiy92ko4N8SpKTnzez1yLzrIkGI8cFgXtrk2tMYeNrMhrh7d3e/38xWQWpRzwHHuPsMM1sLaGtmR6N/s2VyZKnb488wlPXvjyz6agD9IvOuiwJ7Mbt7kUVSeUpISFgYUKWebJ7g5O4XxuFnUEk3U3b6F/Cou2/u8n8FylndrUcI8Qej+HAUMJoB++VIOX0RM/cgZN/2EgoaY919zcgAt0IZLqjvu28ErduBTYr2fiDKaj/wUG+K468gX9gdUfkVRLL6xKWUtDsqP18TnzVGBu4rACe5+zuxzt2oxL1SPJMyMQh338Td26GS9J25bTVCpKh3gN1dIhfZOReiPnEbd382Dg8Ebgqy1JlA7QjeZSQu9HKzfzyvn9BLygHxvJ7LPa9yWFSJT4nwlJCQsDBggSk+VUTKMbMdUDAdNBeXqMwCD6hcvckqVpX6F7CZmQ1GhgelBFnJ3b+Ncu1qwAFmtnzuXipVgrJZ1ZsqJHDN4R4r3VdcoykyIhgQ+3FU1r/CzN5D8pQVkq4WVeJTIjwlJCQsDJgXcYIyqztUmvwceDX/BZMS1DHoF/wE1Ec9ILKwfijA3hmfrQVsBPQws+1QxlgrSFP7Ac3MrBRlgPVQ4AAJ4g8PNaQSYHmUKY6NHvE7iCz0lJkd7DIO2AP1P+tTmJ0FEY9WinU2BWpHiTdPlpoRfzZB5dj8PdZDvdcXon/9IpI9/AIFx1dQRvmiFVQcVwBuBPbJEa/qBnlrX5ee8xikprUM6lUvG/taGpGtGqGs+zszu8fdT3D3t4FNTI5D/VApf5FE/8Gl9B0wkjHjJ9Osfl1O6d6GU7q3ofejw8qVjBPhKSEh4a/GgrK6Wx6Vef+F+qhtkYJR78hIT0JqSBNc86wOfAW8HGu9hPqsoF5oS5dI/z6oX1ovep9vICLTSuRs4FC2+R5FLwUo4+wRa72PMlNQSftqd29L9IaBxiYlqBfj+53RHHCnOOdylF22QuXtyyLDroMIYR2R6pNTyHIvBf6LZl7PI0hRZtYozj0fsYeHA7j777HOaOT+8w2hamVmTSIIn4BUoRZJF56M4FQ6fjIOlI6fTO9HJQZ20a7taV6/LgY0r183EZ4SEhL+ciwoq7t1Ue9yaRR0Ds9IORG4TgJuIafpizKxXUxG6IMpBNl7gNPNbCh6acikBk9GmepGVtAs3tqkKnUkcq1ZEbjXzPq6+62o9/tKkKleA6abWW3kdtPXpKNsyAh+OmI/3xffWQIFu4lxrX1RdvtC/NwIBeIVgfOCkFQTiVo8E9/ZHAlX1EPBtjHK4ruhoF0/7j3f824Va45BwhSvx0enoCx/Esr+F8nRnYoITpOnzaDvgJG8edoWKagmJCQsUCwoq7u7ox97FhpN6Z9b93zk9/oqklfM8AvKhCehUnG9OH4VYiY3Q6pK57n7zyhzG4jGdzz+PtHL2+kNRGMut8a++rn76u7eGpGFPnD3P9z9BXdfO4hHm6Kg+6K7/+DuXZBVX11Ubv5n7OvfKOPujBjSSyHFp0dyBK4HkaxiJnn4PDKmXwJJO9aK0vqjaFypHXAHOV1ll21gc+Qr+2BurftR5t8CBf8K8XcnPiWCU0JCwsKMBWp1V4w5qDfdgHqiHRF56LI43hVljM1Q8Ds5xmCqYgNX2T7mhixV2T3ejoLbQDTj+xazko96Ul6JqjKC09HAMy7ZycpQtlaUli9H2fxs8XcnPiWCU0JCwsKMeQmyZcSnKOH2imNlMLMjzWxYlGuPRj1IzKyhmb2MCD95L9UNgA3MbCoiUq0RBCBc+sFHo37kHsQ4EAqEe6Ae6vMoa+2MAtx04L9x/e2Q9GDetm494M4ouWJmvUwWdINQtvhZBH5iH+Ni7QPNrEkcP8nMhpvZh6g8m831rhXXq4mC/6rAp3HOFmY2Io4dZwV3nF4oGzfkaNQc/RttAPzTzCai8vxRZvbfWGuPeP5rxHmgykEnYGQ8y02B58yszO1oUUGysUtISFiYsaCIT1MQq/fB/Jfd/QZEcNoUkYsmoYySYMnuFNe6AZGaQL3IiXGNjeIan7hs4KYAp8dn96LSMSgY/oKEKO7LroHcb6YjAtWuwFfunp2zMspKl/cwDojjY4HOMd4zMs4n9nFElMUvRASoMZFl3oV6vn0QWemAOOcOYJ3Y7zBEBvvZ3Xuh0vPxyB/2JlROJp75S7FO9hx/RX3clV1qW4OAme6+SPVlk41dQkLCwo4FQnxy94kxmtMI9R1HIw3iX5Ca0TuRXY4HdgaeReXhmShj/ZpCKfe/KLP7GP2OvcPdM4LV58D5ZnY2ORs49GLQHOkPrwE0MM3UHIuC+1lo7tUjY62FNIJnAh/E+M210cvN5nynxX1+Hdf4DRhgZjNRhv8FIjLVRJKN/0DZdWOkX3wbCowXBfFqCSShiJkti148DkQBeaa7j49nOSKecRb0ieODcz9ORAIWtT0nyPF3R7KxS0hIWNixQIhP8VkrFNRudvcW7j481hwdn3+NMsosJRmPZA0no+CYSSFOQi8LUymoKGWYgHqaM1E2Nz6O10QZY213Xx7JKzb08lZ3E4CtXFZ3oxERK3MTeoRgN7v78Tki07vECE0QktoEwep09ALwBVJjKgH2dPdPkLLUinFOP3dfHfWdl6OQ6a8c93YHymbrmMzfs2e5SjyHynAt8H5FAfbvTHxKpKeEhISFHQsV8WkOmCsLvDinV8ywbhJ/9qvKhaxiC7zZrmVm+6J+bd+i403Ry8FB7j4z2L9zUmPaEXgzWNLZvc9XEleGvzPxKZGeEhISFnZUKcia2TYZwcnMsl/uZRZ4oXrUF6kSvRFKS2UEJzObAGyISr/ZmnujcZ2eZvacmTWiQKYqDbLS3bk9jkYztZ/F8dpAo8jOZphZP9R3XY6wjnP3jIh1OCrPZiSrH5Bt3tAoMzdBtnWbx3WfBBqa2RQzy/Z8XszivonM0TeK+7giiEe3otJvmTG9mb2AsuTfPbSOA0egTLwWkoUcmzunG+q5djKzTCxjNCJd/TeuVRsFXczsXpPqVhfgTDOrGcd3CnLVIJTlN2URQ0WkJ0OCFBv1eYn+g0srOi0hISHhL8Mcg6yVt7RrR8HSrswCD0kO/oAC2SVofARU6j0Tjaa0Iizwgk17FcqwHkC92mPd/TvkyPMIYseOQKVOUBl4RzSLehQKjD+bPFavRnOt26GS8MtmVmJSSloR6I7KqZni00RgyZh7vRIFu5ou67h1UG91+zjn+djv+fH9dVG5ukms9V/0u719rJUpLtVC4hZ3IIGMPOqgbHw91MPtE+fUR4xrkBnBHvH3H1G5/ej4N9iZQjC/FylmvR/rHhrHB6Lgug+aob2VRQgVkZ6Asp8z5acUaBMSEhYkqkJ86gp8ns2FmtkDwE7ufpFVbIF3HsroQCzerVBPdgkU7EBBqSFwQW4PWZn1aRRctkeEp8yJZjoqDWeZ2eDY22qo/Lo2CkJHuvvX0bMcgMZkfkIl2btjrfeBdpEVToh1M1bwpij77AI86+6TYq1HIkusgVjL7+b2vRR60VidgqrVnijwLoMkGIcABwZbeS2k7HQhKgG/FOfsg8Z8hrj7RArqUV3jfi9GLwTjYo+grPbbeOZT43ncgEheGYmrHtDSzJrkWNF/a1RmZZdHpvyUmMYJCQkLClUpFzen8AsdVLpsDuUt8IDxJku7rijolZGC0C/6/h4WeO4+DWVXS6Ce5Aco4IJGcv5AAaYekhIECelf5u5rufs6KJg1R6MuQ+KPATuZ2ZIRpM5DikqrIZOAbIToyrinJVEgOsbdZ8beXnH39cmJO7j7RHdfFwX4RijIXR2fbRXkqZ0Qo3jjOH6Pu9dEmeXrMfYzJK4/MO7vD2AlkywjQOvY1wpmNsjM9s/9GwyP3unaKPPNrO4eC1WnpVCl4MA4fgGSdqwTe968ogD7dyU+VZXclEhQCQkJCxLzjfg0NwSnyAiPQiXhZihg9o6PK1N2qgwVkoJMzjmnowBfjO4oKDeL61ybI0vNYh2Xu8eD4pwR6CUhj55AP3effXol9EYl3i6oh3xq7l7WRVl8d9RjbV2F9UAs7tfcPdMuzgJwW1RePr+ik/6uxKeqkpsSCSohIWFBoirl4vlhabcBoXYU6IiyzlPRbGhtVNK9APV++8Z1a6H+Z7aPFSMYDkdZ6a0UNI1vivnVVnHsITT68ln0OmsC35jZqqiU2geVsV+J9dsC75nZuoj4VAs55xzv7h7KTzeizLAOKk/fkbung4FVzexVd+8Xz2UGEtdobGZPuHuP+G4d1GNuiPSZG8XxEmS19ysK2q/Fs6wL7G5mG8Qz/ghl8ETJ/lwUrLM+MWa2EwqsM1EpvKmZNfIwYvg7IW9lt2zdmpjBL5OmYTBLTzaPpPyUkJCwoFGVTHa+KzuhgNkFWdm1RuShjH27LBK674iceLLM7AkUeP4v9rQs8J67f4H6o3vFOZNiP8MQA7gUWet9gyzmvkeBbUsUhN5DJelMi/gGVFreGfVYt4lRoWtQMOuEXHBWym4miGAtkaxjHpMplIt75I5fBVwR6zei8LLzcOztfhTk10NZ8/uoFL0DIn/tEc8Z1Iv9Os7Lx5yvgA7xTPoSDGr+Zii2shs/eRq/TJoG6GYzdnHz+nXZd/2WydouISFhoUJVMtn5ruzk7sNDIWm7OO8bopeICENtzWxTcspOQap6AykeTQBezZVm/4ks65ZCAfPoOH4YcJ27/xJZbpbFnY8YzGvEWm+5+09RJm6IMs1XUR94Z1Q2XguN1vyGGMxv557R5ehloLjnWRcFzqXi3g9BgXgblGX/B/W4S+IeX4xRpDPQy8zl2axuVAsyktlPFALmGfH8miJW9cPufh6qCNwfz7kGUBozun8rzIng5CigvnnaFn/dphISEhKqiKoSnwb7fFR2ivLtROSjOh1lvFnAvwWxZIn/lkCZs8yaKDD2JmeD5+5D3L0z0iG+3t1/iY9aA63N7E00RpMJ5H+PSEdroUCXlbKbI73i5kGEGh33OhPN+YIy6JXRCA5m1hwF020qeHaOAuBQNKI0gCgRR+a/FnoRWCF3L32B/sBJ7n5l7vgzkfXvjYL8F3G8JP5dxiBy03lx/OK4tzpxX3tXsL+FnvhUFeJSIjclJCQsrFggxCcUOFugDHIdlBVeGp89CbQKFu0LSEwf5tLuLXed1VHfd2/glgjwVVmrGEcBJ7r7isCJFEzjrwROzdjJRVgpgv8+wJXRD/7TKFaPmtP3FwXiU1WIS4nclJCQsLCiqsSnFc1sG9RLbIR6hOVQRH5ay8wujqy1KxqlqWtmmwDnoExtMnBMzNXWQBkw7j7OZN92DspaM03eDYBNzOzo+G4DM2vn7lvE9TvE/dxqZqXuvgPKRN9192lmdiIqqa4ea+1oZhegFw0zs0NRhtrCzC5BDN/6SPDCUJn6+NjLvsDm8ffOSD2KWP8AM9vf3e8G9jGzXvG9ZVHG3Biob2Yno4y/DlDPzOq4+xQz2xL1XTczsxPQbO3nZnYdImz9gJSfmrh7/bj351BP+F4Ks8h57A5s8XciPmVkp9Lxk2dLcKqM3JQnSzWrX5dTurdJ/dmEhIS/HFXJZN9HgekmoAcKuq2C7AOAma0O3OfS9j0TkW4y1aePUGB9EJVUb0JB9XngAXdvh0hAS5lZu8jWPkJWc8NjLdy9l7u3jPLzF/Hnmdw+947vjsgd6w90M/morhD3+2WstYzLBu5QZJt3n0txaiYKVGuj3nIJ6o+OQYFvVzRfOzX2tXLsaRPUK/0JeNbMGiBz945IkGMyEun/GWXup6IA/UKs3TP2fAPqe5+Cyt//CfWotsi2b2VEwno0d599kSpUGcxsNRM6o5Eo+JsQn/JkJyhPcKpftyYN6tWcLbmpmCyV1J8SEhIWFOaYybr7dDO7FpUbnwFuR4HoGjO7KvqyxwJbBcnmF+BmIGOiDEeZZy2kKZwF9uOBu83sGBQg3ka9w/1QMJ8eP2cygQCYtIR/ZNbkZm8UrC5GRgEgolB3RGIaBUx194oCTWsKYhh9UX/5M/QikOkRH4aC2+qIiVz8G/sKJDKxQfy8Bhormhn3PIpCifn/0OjQx0iI4wfkNdsFZdMrIuWp6XHOnvFzQzM7MPZwcjyP44B/owrDZmZ2q7sfip71/ijDHQlM+bsQnyqzsKsqwami85P6U0JCwoJAVXuyo4D73X1Vd78QlWFH5IlPLmWnW9Av9ePiT0Z82hYRgJYGDnP36e7+jbtvGr3XQ1Cm9q6793ZZx3VAhKEyM/JgD5+Kgk4+iwVl3MdSGB8igsrXwH9clnPTmRXZKMxLcc4dSKt5ORS4B7j7CHd/AwXG7CVgSm5fOyH27k5Eedvd3wpyUwc0itMaMZpx97dRYGyCsuRf3P15d38fvZxMQpnvOKCPh3pUZMWZstQtsdbVLsWnLYH/RYDNiE83A2dFX7jCHu7CSHyaVwu7ZIGXkJCwsGBeTNvJ9WlrALe6ex/gOjPbB5U536bQp/0FBcizzOwPlBnXQoFvSeAEd/8tt/YTaJY2+/l84EjESn4UiTFMiM96I3LTGmiMaLSZtUFBrRXwuZmdi8aLMLM9UAl7DWRA0M/dZ5iM4j9BzONv0AztFtFL7oPYyFsjtahsX/3QS8QXZvY1UoTKPuuNXiDqAZ+6+88mw4L7kErVt6h8vpnJKq9D3OP3qGT+AXB59KFvQuXlhsDb2fhSlJKvjT00MLPd3P0RM2uG5mmvMpnAVxhh3P1mFIzp3LnzQpHpNqtft6xUXHz8rzg/ISEhYX6hqplsma1doAUqzVbkzgNy1tmZ6NNGBnYJyjQnIKu4HVGgmQQs7+5lPcboe2YG6Rn6ogAImt09FjjdzE5BpdNpKFg3Q9ngmSioT0AEpiWBmiZTgKzn+xoKmnlG8njgUndfO7LCZ1EJuB/h3gPsgmZ5X0ECFRMJMhZ64fgg5nx7orGjYcDKJkej6YhB/QB6iTgKCXFsiIL6WJdx+6eISLUhKlUTPe9xyNwg+7c7A83n7ocy7UyNqxMqK9+N+sd14t4XWvQfXMpGfV4qIzvlYcjario4pXsb6tasUe5YUn9KSEhYEKhqkC2ztYvMqScqw37u7l+6+1RE4Nkpvr898Jm7/xbnlKDgVBuVhQcgXeLbYu0ZFiL5URI+iUKPFAB3/83dN4ny86WIlPR/KMDd6O5N47M9gZfcfV93f9rdV0B+su8gI/bVovw7EmWYy1BeWGIqyixLTBrLm6HS+BXu3gwFrreAMe7ezd2HuXsT1Ev9Le5rHaQ29QBiD3dBgbZrkKteRebzMxBRa4v472PAsia94ndinRHoJeYlM2uLSu6lFGZ+DwYuyj2nn+K/T6OXh93i/qa4jBIWSlREdsrDgUcGlVaJvLRzp+ZctGv7pP6UkJCwwFGlcnGQn8rZ2qFg1NjMekRvtj3Q2cz2QqXhA+L0cygIIXwGHO1SV9oYZV+jUBn3XTM7HXnSvgG8iEQenjazIe7e3cwuRGSeXyn0ZFujDPUVFIDKifoHsvnZDkXHmwDPFxGCGiJt5V8RwepRd38SwMwGIJeh11BAzWMTRGDKdIibo0C5CyJQTaTgXvRulJmHoQrBo0isY7qZHYbK3C1RtWAfxE7ugQL8s/E8VzSzTERjJJKOXMLMxqBRnx+BFd396cj2F2rMb+u6nTs1T0E1ISFhgaPKYhSes7UL8hPABxn5CSlB3e+ydNvcC7Z2B8SozIFIhrF/HH8D9TinAx2jpDwGWNXdT0Ml2uHuvry7d49zznCJQdwLTHb3S5nVuWYPCuzirGfZA3jY3Zcquq2vKZjCg7LQlu7eCLF5a6CXhOwZdEcl3OlIkCKPveP+W3luFtXd73T3nsyKvqg03dPde7r7H/H9x1AG/BLQzuXjezsim/VA5fC3UBaciXqc4LLVOwW9nLyARqhOzl1v0wr2sNAQn5J1XUJCwqKIeVF8KnPniV5fL4rGWszsSDMbZjIsPxr1QTGzf5jZh4jYM42C2P6mwNYxCvQZsEZkqPk1d0Nl4kzk4WeUxf2AAmLmXJMF2CdRRvxqnIuZnWRmw1HJ9Xozy66/AvBC7Pe/iCzUOlvLzG5GZeoNKB/Ie6K+6eFmdl/u+axoZsuYdIv/kT2fKEM/gsrn5+XW6RDP8vT4bOn4aB0K4hetgFVQz3Ycqiica2YfodL2unHe+siBaGo812diZrYcfCFRfErWdQkJCYsi5iXIzqs7Tw2U/e2OyDkgE4JtIytrF9+7GCR4YWZLo/narymM9vRHwebfKLPLnGtApKAWiCTVjgIpaDAKsANR5ndJHJ8GbBT73QsFs0nRJ74IEYzaITOAV7J9of7xu+7eBjgh1qrQNcjMDPWipxJGCjk8FPfQChGtsjLvF8Cmsa//oxBkm8WeT3bpIDdFzka/omx476givAZs5+4DWQjRf3ApE/+oaLqqPDLyUkaQWvm0p9moz0tJZCIhIWGhxbyM8Pxpdx4UkFtRMFRf3sxauLSEs7nVaSiTbBE/94nzfkeZ2qUA7v6BmT2IAmod4HwP5xo0PlMX9VVnUnDhqU9B4rA9hZeNDYDzIpM21Fsej0hbx8Y5O8ceMzP5w1AWfXfsZ2z8t0LXoFwvehLqR7cys+3c/RlgNVQyfyGeV1PEkl4aGBDCFuNQvzjDb2gs6hIUdI/nb4SM8FRZPzaTVGwe0ohAue9nak5A6sEmJCQsdJiXTPZPu/O4+wXuvmRkZhcAr/isYv3jUQB5MX6+EAWqlVHGXNZAdDnXnIF6olcCmIwAHBkMvGxmD5vZ8vH9x1yOQLURIeqyOP4Ikn5cApGMznH3MWhudiwKflNRWblhXL418Cawn5m9E7PDlboGRS/6SqR/vC3wRQRYkC/uP+O53EbM9br714jwNRORsg53CXqUokx/bRSQB7j7vblneGGU5QcTJu8LG+bGym7nTs1nq+aUkJCQsLBhvrnwVAafjTuPma2JgsQRRcdLUPC72t2/jIBVTOSZE2bn9JNdZ19UNu6b2++3LhWq1ZDY//JzWGuunH7MrCMidz1WwZ4PBo42s0HoxSSz/MPd33WpanUBeptZHZM+8k5ofKgZsGTcEyiwt43vL4ee/yxY0MSnubWyS2pOCQkJfydUqVxsFSs7lQlUmBx4zgFmRhA53OXA8w9U5q2FAkYbYrTHzLZF/dQfgePN7PjcKM3NwFLAFSbT92mIifyOmS0X+37TzDYiXGlQRlnHzD5z96tQWXUS0MzMPkGVx4Zx7V5IRKIF8Dkw2czWQb3P13O33hZllDtScA3KytL14jujUQbaFGWL36GguxGwqxWcfmaY2QTUS17PpHpVQ9uxV919M2C72NN0xJT+tujfYRmUTRPPow16GXgdsY2/QuIV98Q8Lma2A8qC3yBHssrgC1jxqX69mvwyadpsv9Osft0yV53KNpgIUQkJCQsj5pjJhkpRRcpOZQIVwMMo2P0DkYgyB566wI4upaLbUbDNSrmPAGejQL06YXoeQWkFCj1LgsizGmISr4b6vzsEkWc6ynDPQGShY0wWeI6C3wGImHURGn0BmRYY6sfujYzahyBy0gZRrs3YvP1irTHAEHfvhPqv2Txsf5TFXo5K242RgcA+QMMgHh2B+rv93f0GRMA6IO4lIymBgmRnVBJegshkc4Ie5yOyVgNE/voWBdd1kMJTXoayaY4o9j1h8r4wof/gUiZMmT3hqW7NGmzetnE5oYqKvpPUnBISEhZGVCWT7UooOwGY2QPATu5+UbFARZB9zkOBBiRaf5EV3Hkmh7LTaSgb7Bl/6gM9zWwYCpa/o6DWFPUur0RB61F3HyWCLr8AuPt3Jo3kzOmnBjFjiwJ15n/7IxJpAJWHl0IvB00pEInWAC4zaf3WjvMz0/h3gZ1Mfq9/UGAwD0DSiBuhgPeUF5x+MmnIGihoerCLt4j7aRH3sTOyuGtJQRf5DZSpAmyMKgXLxV5fiVnc18zsv2gUajp6Ccl6r/eiXu2EuHbmALTQoO+AkUybOWtuasF2ynxgZ9e3bZ68YhMSEhZiVKUn25zyZcvRFJSLygQqgPFm9gUKyj3j8+NdjjodUTb8fogu9EPSh5kIxX4o6xuNAs4d7t4WBdp74rqtkQD+K6hfWeZnGwIQy6ES6o8oeIKC2GOo1FuC5mlx961C5KIjCkK7xvEXXJrFHZCI/+25Eva/kGrTcigDzfrISyLlqFYoUA7J9mVmNWLm9vK4p3dRyXp8EJe+Rllw9jyviufZGgX4Z2Ope+PfoD0Ktl/l7v3seFYboxeDTPHqX4go1gq9EFSYBi7InmylfVSHr/psX0Z2qux7BmXfSUhISFgYMd+IT3+G4FQMM6uHhBjOquDjYmWnM00av9m5S6ESdN7NpwQFxfXRzOlDkUlm56yH9IyL53uhIMWYYW/gTpet3HbIC3cJFPSucPdiQwPcfUYE8hZAVzNba3b3n9tXMSGrQhJV7vt/mijmC1CMorI+avHxqn4vISEhYWFDVYJsRQ48ZdP/ZraNheqTmZ1GwYEn+/w0VM6cCdwV/dxSoLWZDYoS8V1oWmNVxJT9PJSKVgK+jXNGo3LsICQ5WJMwSA8FpQEoSzzfpDJVJ85pgHq7L8UeGsU5tRFhqpmZvWuyucPMWpnZFCT4cJuZ3Ri3cgjwmEn16S6CfYzELy4zs8no5aKPmV1f9AyXQYSkixAhq76ZnWxmHyNRi4axX0w6w7egcvaLZrZa3OfxcY1bgSPN7Lrc+i/Fd44xswNQpr8WMMIKik+vmllXFhJUJkBRUX91blx1klBFQkLCwoSq9GTzBKdSlOHtA+VIUYehHuL7aLb1s/i8PpI/3NvdHzWzHsDl7r6Nmf2GiE9PIyLTBu4+zOSDOgaNpAxEsoh7Ag8iFaYOqMf5DbBp9CRvR+Xkrdx9qJk1RIzk/ogM1BWxiGtREKQ4FL0wtI/PL471QT3h69397NxzGIWyw4/Q2MyLwJPufkcE3sGoHF0P2NPMGgPT3H086in/hmzsPHrIp6KXiCtijz3NbCiaB+7h7s+ZfGT/4+69zOxh4ClUJd0JBW7M7FJEfFoJvUQMAp5w90ZmtozLCekV9IKyCrLTW6CoTICiQb2anL3jmrOUf7Of+w4YyZjxk8t6tcXfK143CVUkJCQsaMwxyHoFDjw5gtPvKHjtBFyDssaTkZA9SCVpFaRIdBYiOGUs1wOBO9Fo0LPAmpFdzkSBZMn43tLAMHcfYdIFHhzfeQKRpzZC5KjxKFMGON3dnzGz21Gv8oX47gG5HusBiFX8pZmNAq7NlZKXpXypmLivwSjb3gE40AtGAE4EPcSozkhbd5lmWZdEwTkjS/0fymA/Rln+D3FOX1RduNrMJqGXgv5xTjukl7wletnZ38xaxL7GUxDt+BYxte/Plc0NZf4LhSl7ZUSmerVKKg2GVXHVmZ1QRQqyCQkJCwJV6sl6BQ487n4WIuB8mxGcUHb2rBcceC5w9yVR+XNpFKSPjM8Guvta0cd9BTn6/OHu0xBbdxgi8qxCMGPdva+7t3Pp9DaJa72BHHGeQsFqJiqV4u5TXb6yayHXnpdyt1WPGBty9+mItZupOE0D7jezV81skzg2hoLq00zgMAsFKdSX3Rdlx/si1aYPkf3dd6gEnukm4+5vI63lJkgq8hd3f97dt0KjQw0pjAj1if8OBXZ19ztReXhpRGY6BbgsRyL7H0GkgjJ7vrVR6bwfRVgQxKfqEpRIQhUJCQkLG6pd8QnmjhQV/dWj0NxnM6R33LvonDPQyEomIViCMtZe8d9dYtTmzyCzu+uEMsf7QgRidqpPc0WKstkrNZ2IxPxbAHdQmDn+FzKTH4wCcykqAc8WXrDnq41Gh4o//8uJT9VFZEoEqYSEhIUN86L4BAU7t5NQFtcAmGhmK7n7N3HuJYgRvATKAneO4+ui8ZxVgceRgAOo/9oZMYWnIzWinU0qTafGNeqj/mtW/jw17uV/cc7jwDpmdjiFWdN6JvP3jtGzbQp8GiXlE1CJeBwS/28aJCNQL7Y1yqxnIGLTKXHOIUGYuhSRjPZDRu11EFnsYERYugdlv5OCVLUN6i2/gDLq5sCYyDp3AVaJyvWPFLLSNdCYkKEebg13H29mqwIHmtnu8b1fgRvjGc+gMDc7EwX2TDFqgWHzto25951R5WrX80NQ4pTubWbp9SahioSEhAWJeVF8giBFoexvA1ROvY+wjjOzDVEPcW1Uwt0aqQ+BgmcJIlItRZRugcNRMPsHGuU5HfUyv0KygL8jotXVuW1ORsFwQzSusxkyfN8rV0adDjwa35+C+sGZ9dvuaG7XUe9yvzhnVxTYvkTs4q9jf1cgMtPwOH8i0DfOuQoF2R+B3Vy2fUuhUvYj7n4tUm76PvZ7DSpFjyAENoA9Y60HKPRxbwL2jeO/xnqgXu80VGbeHAlaDIiRpinx/c6IjPYJCxj9B5fyyKDScgHWgN3WnXPPdU7YuVNzLtq1Pc3r18WQUMVFu7ZP/diEhIQFhj+t+ISCWEaKuhK56dwOPAM8HkziH1GJdRgKBM2AXmbWFAWDeiiQ1kcl1sxD9hHEOK6Hsuf/c/dxJkPz2kgVqo2Z3ejuR6Is7QYU9B3NlD4d+70EsaFrIvH9Jdz9nCg5v4Z6qGUCGgTpycx+j3WPdPefzWwn1P88J/bbFtgDvah8j3q0J8b1D3T3SYRtn7tPNbPv4jzc/V0z64dITy2R3OPN8TynAI+YbO1+IZyLEKHqufj8VwqiExPjvt+Pn8+L/S6PtJw/jD2+TGS4CxIVkZMcePmT+dMPrgpBKiEhIeGvQlWCbEWKT+tlP7hs2jKrNszsWhQwnoif70alZAOucfcnzawzMDSIPgS56FR3H2tmJ6AAYig4bOghU+juq8X3/wW0jQAL+j19OArQN7lE77P9/dvMnkKjQ51zx6fEPGtndz82d38/USBAPYYIVdlzeN/dN409fIFUmZZCLw8TEMv3P+6eNxnIRpmaEKNPcf2zo1T9DtDd3bPIUxPJNk4HrsxebtBLQP94JtPRqE+GduiF5lNUMsfdfzDJQ06N778YBK9yiJL64QAtW7Ys/ni+I5GTEhISFifMV+KTFSkVhZDCGiibbQ5skWPrVoajgBPdfUVEAiqnuWtmm6PSbd66beMgI22LBBk2LVpzb2YdyakMvVyGBpvEn/3m8P3KiFLZfsupMRWd2xMZEORTu5XiZWAf4MrouULlhKgngVYue74XKGgtz26tMvzVxKdETkpISFicUJVMthToaGYjUen2c3LjKAAR1G5DKkiHuPSJQSSeZZC4/hKozLkB6o1ubNL1BY3pfB1/PwCRhXZDAhQTc9dZG/VSGyGx/4zdu6eZHYoyWke9ztfM7DYU9NsBz5vZTe4+wcyuQP3L5YClzWxfd68fa40yqVARa3VFylANgPfMbGx8thwiSm2GyuOZnvB0RJQaGISxB2OdsmdmZq+jEZzWiCi2orvvHKzjayMYTkE6yJ1Mwh2bIpUsQ9rMGwJEGf2fZnYM6mVn2X5X4OYgUBnqK3diAbrxzI3KU0JCQsKigKoE2UGICbtl/P0nRITKYylE9umPlI0y1EGKSk3QL/qxiBX7efQKj0MBeDwFI4AxiAR1PCL9KEqYtUTB+UtyqkWRLR+PMuYlkBJUlpKdiIJRb2AkEsfo4+4nxrkHomD9RfxcguZpO8Yo0f2IWAQqmf/m7lubWU80s+pmtizwurvvYGarIG/XL4Mwdj8a9dkJBegn3H24u29iZm2B5+KZPh7XOBcJb+xiZuujedjTUTm6PhrBGY7GmkbFnneL9Tug8aEzYq3RqNQ+KcakhsZzWiCYW5WnhISEhEUBVQmy66Jf6reiTPY1YC0z6wIMjN7rSUidaDOU9e7v7j1QkDguznfEAu4f6x6NstJlUHDMxPAPQ8SniShjPTqOn4V8ZpdBhKT1USbbGAWh91FA/okgBbkkBTOh/w7kFI/M7OtYa1mgXZCuvgHqxgtADdTfvCVO+QxYNchXP1MgSnVA86tDKE+U6oEC40roRaIxYhXvFuf1RC8NB1Gw4FsXWCFIVkugPu84lMW+guaCZyIS1JA454L4+b3YVzZv2wq4KQhUteN5Zkzlvxx/RuUpISEh4e+OqlrdDfaCpd09QHN3PysjN3lYxyGpw1MiwOLub6Ie4QooEN7m7iPis4GhxHQrIitlAXAS8Ia7r4LYt9nYyQmIpbwKYsleFeu8jVi/rVDGPMjdn8/t31GG2BaNyxDntULl07HACpFhTozvT0WjQi/n+qUzECFqEvAmBZOE1+L7hsZqsr5rrbjfNWKM5lTUv82ufw4KlC96Qf7wVaQ73AH1nZdD/eyPEAu5Gyq3f09BdvKPeMZT0L/n0rH+W4hwloloHFgZ8cn+AsWnRHhKSEhYHFGtik9VJD6VWcrZ7C3azmHu1ZNw94Pi+AgKBgD5a1eVeNQbBeouKPhlxKsP4pwOKIj3r+RxVIRiQlYf5NAzBPgn0kqeES8mFwPPoxLzEApqT5Xa+bn7uy65yy5Abwunnzz+KuJTIjwlJCQsjqiq1V1HCzs7JF1Yzj/MzDY1sw+A/dEv+wzHoJ7su2gW9FsK9nRbmtkIlJleFQF5aURU+tQKFm3PxMjPDsA9cfxMoK+ZXQRshcq8FyLB/a5EydTMapvZg6gf2y13vGsEsrOBbcxsl9yedzezjxBrdyzKdkFl66FxL+0Q8xgkB/lGrHcJsFrcSymaCR4Wn12UPTczW87kjLM1mt1tEGttjkrGUHg5yTLjm1EJexnUH/80jo9GZeerKLLzi2vthvq4Rmg6Lwhs3rYxVnQsEZ4SEhIWdVQlyGbEp8Piv5tRIANlGIVcdYpHVL5BgbUDCpI7xXdB4hFvoAztPjRf+ivQzN1ruXstFDQ/j9Jy29zxm4Bx7t471tse9SPboDGWVyKbOw0JOqyOyE1Z8+8j9LIwFgXym8ysxKRQdTgK1FugoJRlzv+NMZlOFPrFAHcDnaIkfDsq336BesQliC3dFfVWn4hzsn3dE/s9LY4PRHO7HdF8br1cKTlTb+qBAul9cbw/yroboEBaC/jJzFaO+dzjUUbcigKD+y9Fdao8JSQkJCzMmF/Ep8ZIuGF54Agz2zbKlNegjGwY6nX+hoI28fN2KCvbBbGKyQUV4noV2bO1J4g/oZ5UO9bYAQWUK9Dv8RNQ8B0W99DYzCwYt3sg2cI6uWtsjjSN30YvIJmDzXMoEDeOdadRXpLxKDObjoL4ldFfnm5mPwMPxTm3e7gToZeNcehlYSgiNZ2K+q4vh4jEdxQkKEHqTcPj2se4fGpB5LFzEEO7zM7PzDZGSly/on+fc7xgzfeXorpVnhISEhIWVswv4tP7IZJwL3BQBFjcfYa7H+Hua6BM9ycKc5qHoqDwPyT4kJkOYGbHhKLS0nFeMZqhAJqpKf2GzN+nxpr13X0mKs9u5rLU2wcFokzN6VlgRxSAjwxS0KOxxy2QqtUKSOgfd98CZaLLIlWm0+P4tXG/G8TzvCS3z0mxt/FIkSnD8u6+obs/hwLp8rFWZubuKLjn791jvcnxXDIcAVzs7m0pb+f3MfBKEMgGUTTbnOGvID4l0lNCQsLiir/E6s6kVXw3CsAz43BlCkZzssZbD5jk7lnJenYWdJWiIlLQHAhGuPsZLiWqe9HMbR47Am+6+8+5Y3NSoiKyXs/9/FgEzJ3RyE+GWQhZZtYM6Sdfk/venAhkxdevduJTIj0lJCQsrphfik+1kSrStsBGZjbQ3b8OQYe7UCl3HCrHvhNl1/WB/4ZoQ5mCUfRSL0DBYwbqJR4Q5KDbkV/sFDNbKwLtOJTBnmtm56BMefXY2s/IkaY2YgEvC4wLEYq+FAhctVEJfGnE6gURjA5DZfK8ShOopFwXEacws26o9DvBzF51982CyftYXLsEZbJdUbn995jFnYl6uL/EOpkSFcgcYVUzaxRl3uPMbPv47GvUG56MMt7JIaThQU5bF/WT3zGz5eL6b5rZRu6elbn/EiSVp4SEhMUZ84v4dAgKFI8iws7FcbwnKr32RhKCR5j8V39FJdKjEFN3X8SSBfg3KtG2jb+PjOOno/7lVCTecFUcb4b6lCfH3G1TCmSlJYExLmOBBsA3kTk2Ah4KItFOKPh+7e4vA1vH8f1QL/XcWOtgL9jmfUfM70a5OnO3aYNeDkAvJD1itGdDFPiyaLMk8EJurUw68joKJKrH0OzrODPbK87viPrY68e9PI3+PVZHJLNJ7r5aEMhWQy8Zq6HAvsOCCLC9Hx3G+MnTyh1vUK9msqBLSEhYLDC/iE/7IfP1JVFgWDYy0vWRQMSBKBCvgMg9TWPNq1A2N42CgtGJSAjiAxS4e8XxdqiE+627/8/MbjLZuYH6nmeZbO2aIe1jQ2pLX0V2NxX1W0EBfzcz2yiuf3SOFPSIydR9aSRGkQlI9DGzNrnz28ff90HjNENCzCILmMujTBbUw/0dlbIhVJzM7DMUyDPW8W7A/mY2LZ7nGUFi2gRlpoPQi9H7aC74fXcfDBDXyWMf4FF3HxWf/VL8hepGUnlKSEhY3DFfiE8oIK3j7ku6e0OUVTVE5KSHYo1VgOPc/bX4eZC7t49M72xUHgUFpDtR1jcFBUdQFruKu69vEr9fCWjh7qUoc14bBe8B7n5vXH+8u+8emWx3JNoAmtmdGvf/JQXGM+6+ibu3QzKKV+aO7xaZ8iVIlSnLsFujMaUVzGyQme0f3/8SWAf1W1sCd7v7u3HOwfE86sY+zolzLo4+8U4oG896rU+gMvgGqJzcnCBkFaFV7u+tgQYxj7s0ekmZBdVJfEqEp4SEhMUd1U186or6qs1Q5nWySUR/dqiNZkI7I93g2+N4hWpIc1J8qgSzs4fLiFrtKRij51Gs0lSCsv3tUSA/08xaQxm7uiMiZnU1s0wMolLSV6CcEpVLJvIZ9HKQmQ7MmiKWR6X7yqM6iU+J8JSQkLC4o6rEp3zW1IIixafcd0YHAWdZVBLdB3jO3acBY83sTaTo9O1s1swUjEB9yTugbH72ICgjR32FstDuwFfu/mN89ijqgd6LgnJJjOeUXcPDBD5wK+XHbgD2BB6LfZfBzBqhF4e8QtRoJIwxEdnWvYZ615kiE+4+3sxeRupSPwAdclntg6gMnkdPpJZFbo0LCaN2M7sPWfRlZLRbmRXfIXekoejf4oPifVU3Nm/bmHvfGVVu0DkRnqqG/oNLOeeJj2fpZyckJFQfqsMVrCqZ7PvA6qEgVAsFgCeKvvMEUjYCiTO8FASjUYj4hJktiXq0n8xhzf4UGLabEUHBzOrHd0Eztq9F4B0FrG9m9SL4bgmMiOu/HPsh9vd4rNU0t/cezOpOU5nJ++7AU+4+JXfsceSNW2Jm9dB87QgzaxykKMysLvCPuPdfUM86yyr/kb++yQKvAYX+LWZWI/rEmaduNkO7LSoD743K7HnURi5AbRGhapsK7rPakFSe/jz6Dy7llIeHpgCbkPAX45dJ0zil31D6Dy7OI/885pjJuvt0MzsWlU5rEMpFZnYeBeLTbcDdNqsN3HXAHWb2Mfode4e7fwhQ0ZpxTh/gXjM7EfUlD43jawB3hRrSx4hIlSk+9UOZ2nRURr45zjkVeMDMLojjt8Xx40xWdNNjvwdm9xvs5xWpWLyhJznRjLj+CDN7DhG5ZgK3uvtHEQzvihGlJRCb+am4xmGIYDUTBd2Di67xQLwkZKgJvB4Ept9Q5t3L3b80s+NQL3Yp4EMze8bdD0XB/rHcvmrEc/tLkFSe/jz6DhjJtJkVCZ0lJCRUN6bNcPoOGDnfkgEr/7s84e8AM9sd2CaCKWa2H7Ceux+b+85H8Z3R8fMX8Z2fitY6HOk107Jly3W/+eab+bLHlU97ukI9TAO+6rN9BZ8kZKjs2SUkJPw1mNvfU2Y2KHhEs+AvUXxKWHhRXcSnRHr680jPKCFhwWJ+/n+wSkHWzLaxsLozs9Mq+PwkMxtuZh+a2YtmtlIcX8nMPjCzIWb2sZkdmTvnQjP71syK/WGviO8PMbNPzWx8HN88d3yImU0xs53js9dzx8eYWf843s3Mfs19dlYcr2Nm75nZ0NjXuRTBzK7O783MWprZy2Y2OO5zuzjeq2hfM82sY3y2t8nq7kMzey6IU5hZRzN7J74/0DSSlF2nW+55FStr1TCzwWg0asU4ZqjcvJeZjYjyMWg06JXsGmikKU/4qjYklad5wynd21BziVnmnhMSEv4C1Kxh8/X31BzLxdFT/BQRdEYj0tLe7j48953NgXdd7jZHAd3cfa8gKpm7/2FmSyGlqA3dfYyZrY/maT9z96UqufY/kQLSwUXHl0Pyji3cfVLRZ48Aj7v7f01yh/9y9x2KvmPAku4+wST9+AZwvLu/E593RhZxu2R7M7Ob0bzwDWbWDnjG3VsVrdse6O/uq5pY1mOAdu7+k0koY5K7n2NmzyMD+mcjWP/b3buZiFJvoTLvKDNr4u5jc+ufhNjZy6Ie9Zbx5xJg0+gFN3H3sdHTbuPuR5rZv5GV4DIVPecMnTt39oED500UKlN5Ku7HVgdrb1FGYhcnJPz1+LO/p2ZXLq7KCE9X5On6ZSz2AJpLLQuyLjnCDO8Q5ujuPjV3vDa5zDkX0GZ37b0JfeAi7A48W0GAXQaxmQ+a3aJBKsqy1Jrxx2ONGkjXeB/Kj+o40jMGBbkxlez3gWw78WdJMxsX534+h7XKVJpin/kA2wLNvF4InIQMCgYgoYvrIsCehyz4nkB+vRkZbSryzq12JJWn+YOdOyUWdkLCooCqKj59m/t5NAXz84pwCLKRA8DMVjSJ4X+LLNkqCk6zIErOKwMvVfBxTyoesdkZeNHLe9JuEGXhZ81szdz6NUzCFmORjnA2t3osUnT6jvI4B9jXzEYjYYh/Miv2yvYVM7ZHISu9MWjUJmM3nwD0NbNvkWNQ7zheptJkOfWowJVIy3lmrP+Mu7dGMo4/R0m4CzGmE2NG9yEG9QpI+nIW2HxWfEoqTwkJCQkFzFfik0lpqTPKBAFw929DWWk15KazfGXnF6Gc6lHuGnOjxvQBsojrgCQK++f2NYsak1ViHZdb+85QadoOZYllz8+KLPiiDH0UcstphkZpsmB6FHCiyzbvRArBt0KVJjPbARjr7mXyjzlUppA1O9s8ct+Zr8SnRHhKSEhIKGCeFJ/MbBsk8l8DeAXYCJmk/xGfF1u3rQRsAvQzswOQV2w9MzvA3e+Kcy4E9keBaZPsoibS1DFIqGEGcp4Zbma9gFNiD2sA3c1sHXcfgoLV6abZ2jFA7SAfnYr8X6ciTeC3kVjD8mi+dFKUsWua2Sh3b4mC4QQzOxg5DdUBGsWLxaFAY+BXM1vJ3b9BjjmrEgIY5Oz8EFFp4yBcDUIleZCecSvgTfRvs2o809boBeUIwlDBzO5x932Rhd6+pjGe6Uh8AitYA66KNKCbWsE2r9qQVJ4SEhISCvjTik/Ru7wOqQ7tjcqRJ+b7iMBlwAaRMd6KftmPDOLS2URAA86OoADSFd4H9TPfzq11n7u3R+Xqawi9X3e/N9a/DngaSSwOCeLRNcDmkUmPpSD3+B6wURz/Mvb+ibuf4u413b0Wyijd3Vua1JYaAde7BPzboJ7qj0jkogsK2HdRkGhsiwwLtor7PAL4IrLf2sDlLsMBQ4IYIJP1j1E14Ox4Nu+7e+94zl3i/l+KAJvhlXgGDxPl5NjHkLjHPugloFrZxUnlKSEhIaE85kXx6Xbg11AduhkFmdvNbCwwyt17oMzyssgkVwNudvdhZrY3Ui76EGW4TVCpc3d3f8dkvj49r3rk7r9ZQY3pK2TenkdP4HvKE49qA2+Z2WTUl7zB3d2k+fu/eFFYFvg5U2PKYW8K/q+rICu+nmZ2KJIrfCv293KwmL9FGe42cc4K8cxeRlZ+mRpVQxTwTzSzUxDpanTcY149qhmScPwoPnst7r8YXwKbmdmwuN834vjGQB0z2wWZu4+L5/xDBWvMFySVp4SEhITyqFJPNiPZuPuqLqF6EPnng/h8K+BoxIztGAEWd38hMqkeKKj+K85tjvqtLdx9CWRV907ueudQsLjLY3uUDV8MHJf/wN27oUwvTzw6CAW7Jsj8/Yz47EN37xR7+5BcDzmHvZBVHYgVvCLytl0XMXjLaNHu/oq7r0950tdQFFDXRYzn6ajU/VP8/YjoFb+AAn22Vl+Uyc6MZ1qMiUUjSceiLLs+8vM9Ko4/hsaJOqJxpOVRqb8c5ifxKZGeEhISEsrjr1J8qpDENLdw9+tcnranon5uGeaSeJSdcwYKePfObi13/yXWehB4HfiaIqu5YtKXV2JPF9lvT+AKM3sPmbkXP5cdgTfd/WfmjMpIVBVaAxafPD+JT4n0lJCQkFAe8xJkS4GOFkpQKMsrZ11gUnx6EY2/bBSzntm551moJKEMMCNTbWFmHwB1zeyu6K1iZm3N7G0z+wNlwjsX7WdvoKGZZWXfjihzfBgFmE1RfzRTlRoFnA6shUT6sz3PQH3hhmaWdxt6Cvgfyk73QYL82TlbIfZuZxQgM4xG2WUrVHLObOYGoVGb+ojkNTHWOdDMfgRuAtpHaTpTiHobeB5Yzcz2yl3jYOAMk1bx9hRIVDuiLLoGIj81Q6XlakFSeUpISEiYFfMSZAchf9LD4r+bIUWnPC5F5dAfUKn4ojiejd9sHn8cGBCkoLtQpjcZKUJlFnrLohLxpajX+1l2kTjvIETSylCKLOGOjZLpaFRWza4/AQW/qyn41xLXnQZsnJW9A/9EJeP1437+E9fuhILi9ygIZntqBJyLSE8HooD3Xnx8IerLto/7yJerH0Ol4jXdPfOJnYSC8dYoi77SZP03OxLVj8BWQRZ7H6hVND8835CpPBWrEzWoV5OLdm2fSE8JCQmLLaoywlMZ1kUl2FtRtvQasJaZdaFggdcOEYIeQASg/gDu/rOZTaUQFM+LY41RhvgSIkQdCfyKSqD7oEy0IQqQO+X2slv89yqkhoRLuvEnZJv3OyIYZebo16Lg9AIaBcorVtUAvvVQuMrhbBTA3oj9Zj3kvqjnWxuxjhsD/dAs7bLx/d+QCtM/UOn4CDRStAciY/XPXWcV4HmXCTxxL5+a2f1AN/Si4KhMfCuVkKhQGfqNIJ19il4cqgVJ5SkhISGhYsxLJtscafm2jj7pPUBzdz8rAiyI/PONu5+GJAqXjnEYUIAfj4hAWbn2JxSQdg5C1P2I6IS7Hx/jMzcCN3rBfxZEUtqCwvhKhj3QqM1yKDCdHWutFj3MneJ6+YBdEygxCfjvXLTenSirPMDMVo9jByACWEsUSC+O402Ay9y9XZCihgLNTfrEv6J+7URURs8LdKwJtDOzfmZWNp/s7nu7e1M0N/tFXKdSEpW7vx3/Nm2QN+4zVID5QXxKhKeEhISEilHdxKd/ofGSwaicXEqBfLNSqBTtg8qfq1aRFFQOc1BDOhHYzqXSdAcxW5tDRYSsWfYVxytTVroSONXdiwN8ZShBLN+33H0dRIi6ND57EmgVrOcXUOk8f69NgbuBg9x9ZlWel8m84RBEFpsF84P4lAhPCQkJCRVjXolPFSpBZXD3Me6+q7t3ojA+Mz7+Wxr//RKpRXWKn992903cvSsqQX/K7LER0MPMvkZl6S3M7J4oPXfwgibxgxQUlzLMooFc2b5QGTbr3T6G+r0gstMDcf3dgesjA67s+YxD2XC21sPEqJC7j/NQy0Kl4HWzk03mB08DZ8QscTaTfEtFz8uEe1Gf2JDaVrVg87aNC/NMgUR4SkhISJi3IFuhElT+C2bWyAr6vr2J7M/MGphZ7ew7KFAOj5+bxH9ro+zrxtltwt17x7xtq9hDpob0C7CsmbWOr/6DEM+P9dsi9vHbuWOV7gv1kzOJyM2IYObuK7t7q7h+P+Do6LEOALaONRsg0tKAyD6fRP1VkFVddu9Nc7fWI9tvPN/HgP+6e7/4/E4kfFGjkue1P2Jgb4aIVzfM7jn+WSSVp4SEhITK8aeJT165EtR5FIhP3YCLgnzzGtIeBrGDbzKzmSjQ9/GCP+0pUQJeApGCXgIwsxWQCMQywEwzOwF5tVbImI39HQY8Etf5BY27ZOgJPBBBL8Ps9tUHkahORMSrQ+fwfH42s/MpInfF309FBgNXIhZwZs13nJn1QH3Wn1FwBNgTjSA1NLPs2IGop93IzEYUPy/gNNSHvj5+bmtmTX1Wd6F5QlJ5SkhISKgcczRtT1h4ETKLT8X4TvFnT6GXhDfi5xdR73hg0fcOBw4HaNmy5brffPPNXO1h5dOepqL/BRnwVZ/t52qthISEhL8jbDam7X+V4tP/t3d/IVaUcRjHvw+ridifzYyQzVKjBCFICfFCvSlKpdIMwggyCiIoSKLCEKK7sKiLIJKiyMJSoqK9CayIutJKW3PN/yakrGsYZGD099fFvJuzyzm7m2fnnZM8Hxh2znuOzMNv3p13z8zrjLWpVic+edKTmVlzHmTPXSNOTBsLj988i4njOwa1edKTmVnBg+y5qxu4J80ynk/xxKQxvR4LsHxOF8+suJauzokI6Oqc6Ls8mZklrdzxyWpUvgOUpKMUN9oYDxAR6yluPrGU4glCpzkzuWrMLZ/jmcRmZo14kP2fioi7Rng/ODOb28zMauDTxWZmZhXxIGtmZlYRD7JmZmYV8SBrZmZWEd/xyf4l6Ufgv93yqTCF4rF77aQdM0F75nKm0WvHXM40elXlujIiGt7Nx4OstUzS181uKVaXdswE7ZnLmUavHXM50+jVkcuni83MzCriQdbMzKwiHmRtLLxSd4AG2jETtGcuZxq9dszlTKOXPZevyZqZmVXE32TNzMwq4kHWzMysIh5krSWSFkvaJ+mgpDU1ZZgm6TNJ30naLemR1P60pGOSetKyNHOuI5J2pW1/ndomS/pY0oH08+LMmWaV6tEj6ZSk1blrJel1SSck9ZbaGtYmPa7xxdTHvpU0N2Om5yTtTdv9QFJnap8u6ddSvdZXkWmYXE33l6QnU632Sbo5Y6bNpTxHJPWk9iy1GuY4UGu/IiK8eDmrBegADgEzgfOAncDsGnJMBeam9QuA/cBs4GngsRrrcwSYMqTtWWBNWl8DrKt5/x0HrsxdK2ARMBfoHak2FI9s/AgQMB/YljHTTcC4tL6ulGl6+XM11Krh/kr9ficwAZiRfj87cmQa8v7zwFM5azXMcaDWfuVvstaKecDBiDgcEb8Dm4BluUNERF9E7EjrvwB7gHZ9wO0yYENa3wAsry8KNwCHIuJs7vLVkoj4AvhpSHOz2iwD3ozCVqBT0tQcmSJiS0T8mV5uBS4f6+2eTa5hLAM2RcRvEfE9xfOk5+XMJEnAncA7Y73dETI1Ow7U2q88yForuoAfSq+PUvPgJmk6MAfYlpoeTqeCXs99ahYIYIuk7ZIeSG2XRURfWj8OXJY5U9lKBh8I66wVNK9Nu/Sz+yi++QyYIekbSZ9LWlhDnkb7qx1qtRDoj4gDpbastRpyHKi1X3mQtXOGpPOB94DVEXEKeBm4CrgO6KM4hZXTgoiYCywBHpK0qPxmFOesavk/dJLOA24D3k1NdddqkDpr04iktcCfwMbU1AdcERFzgEeBtyVdmDFSW+2vIe5i8B9vWWvV4Djwrzr6lQdZa8UxYFrp9eWpLTtJ4yl+sTZGxPsAEdEfEX9FxN/Aq1Rw2mw4EXEs/TwBfJC23z9wSir9PJEzU8kSYEdE9KeMtdYqaVabWvuZpHuBW4C700GadDr2ZFrfTnHt85pcmYbZX3XXahywAthcypqtVo2OA9TcrzzIWiu+Aq6WNCN9M1oJdOcOka4BvQbsiYgXSu3l6yu3A71D/22FmSZJumBgnWICTS9FfValj60CPsyVaYhB3zbqrFVJs9p0A/ek2aDzgZ9Lp/8qJWkx8ARwW0ScLrVfKqkjrc8ErgYO58iUttlsf3UDKyVNkDQj5foyVy7gRmBvRBwdaMhVq2bHAeruV1XP+PJybi8UM/T2U/x1uramDAsoTgF9C/SkZSnwFrArtXcDUzNmmkkxy3MnsHugNsAlwKfAAeATYHIN9ZoEnAQuKrVlrRXFAN8H/EFxLez+ZrWhmP35Uupju4DrM2Y6SHHdbqBfrU+fvSPt1x5gB3Br5lo13V/A2lSrfcCSXJlS+xvAg0M+m6VWwxwHau1Xvq2imZlZRXy62MzMrCIeZM3MzCriQdbMzKwiHmTNzMwq4kHWzMysIh5kzczMKuJB1szMrCL/AJ8RY+HoJACUAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# plt.plot(range(1,206),train.values[2][1].split(','))\n",
    "plt.scatter(range(1,206),train.values[2][1].split(','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-3536571a9bc3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:53:20.837171Z",
     "start_time": "2021-03-15T00:53:20.824203Z"
    }
   },
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    start_mem = df.memory_usage().sum() / 1024**2 \n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2 \n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:54:18.244721Z",
     "start_time": "2021-03-15T00:53:59.807775Z"
    }
   },
   "outputs": [],
   "source": [
    "# 简单预处理\n",
    "train_list = []\n",
    "\n",
    "for items in train.values:\n",
    "    train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'train_list' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-e61314b08203>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'train_list' is not defined"
     ]
    }
   ],
   "source": [
    "print(train_list.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.DataFrame(np.array(train_list))\n",
    "train.columns = ['id'] + ['s_'+str(i) for i in range(len(train_list[0])-2)] + ['label']\n",
    "train = reduce_mem_usage(train)\n",
    "\n",
    "test_list=[]\n",
    "for items in test.values:\n",
    "    test_list.append([items[0]] + [float(i) for i in items[1].split(',')])\n",
    "\n",
    "test = pd.DataFrame(np.array(test_list))\n",
    "test.columns = ['id'] + ['s_'+str(i) for i in range(len(test_list[0])-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = reduce_mem_usage(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:54:57.351196Z",
     "start_time": "2021-03-15T00:54:57.321310Z"
    }
   },
   "outputs": [
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       "\n",
       "[5 rows x 207 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:55:36.644040Z",
     "start_time": "2021-03-15T00:55:36.619678Z"
    }
   },
   "outputs": [
    {
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       "         id       s_0       s_1       s_2       s_3       s_4       s_5  \\\n",
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       "\n",
       "      s_199     s_200     s_201     s_202     s_203    s_204  \n",
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       "[5 rows x 206 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练数据/测试数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:56:15.971953Z",
     "start_time": "2021-03-15T00:56:15.876344Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train = train.drop(['id','label'], axis=1)\n",
    "y_train = train['label']\n",
    "x_test=test.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:57:01.757175Z",
     "start_time": "2021-03-15T00:57:01.750341Z"
    }
   },
   "outputs": [],
   "source": [
    "def abs_sum(y_pre,y_tru):\n",
    "    y_pre=np.array(y_pre)\n",
    "    y_tru=np.array(y_tru)\n",
    "    loss=sum(sum(abs(y_pre-y_tru)))\n",
    "    return loss\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:57:42.940805Z",
     "start_time": "2021-03-15T00:57:42.928082Z"
    }
   },
   "outputs": [],
   "source": [
    "def cv_model(clf, train_x, train_y, test_x, clf_name):\n",
    "    folds = 5\n",
    "    seed = 2021\n",
    "    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)\n",
    "    test = np.zeros((test_x.shape[0],4))\n",
    "\n",
    "    cv_scores = []\n",
    "    onehot_encoder = OneHotEncoder(sparse=False)\n",
    "    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):\n",
    "        print('************************************ {} ************************************'.format(str(i+1)))\n",
    "        trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]\n",
    "        \n",
    "        if clf_name == \"lgb\":\n",
    "            train_matrix = clf.Dataset(trn_x, label=trn_y)\n",
    "            valid_matrix = clf.Dataset(val_x, label=val_y)\n",
    "\n",
    "            params = {\n",
    "                'boosting_type': 'gbdt',\n",
    "                'objective': 'multiclass',\n",
    "                'num_class': 4,\n",
    "                'num_leaves': 2 ** 5,\n",
    "                'feature_fraction': 0.8,\n",
    "                'bagging_fraction': 0.8,\n",
    "                'bagging_freq': 4,\n",
    "                'learning_rate': 0.1,\n",
    "                'seed': seed,\n",
    "                'nthread': 28,\n",
    "                'n_jobs':24,\n",
    "                'verbose': -1,\n",
    "            }\n",
    "\n",
    "            model = clf.train(params, \n",
    "                      train_set=train_matrix, \n",
    "                      valid_sets=valid_matrix, \n",
    "                      num_boost_round=2000, \n",
    "                      verbose_eval=100, \n",
    "                      early_stopping_rounds=200)\n",
    "            val_pred = model.predict(val_x, num_iteration=model.best_iteration)\n",
    "            test_pred = model.predict(test_x, num_iteration=model.best_iteration) \n",
    "            \n",
    "        val_y=np.array(val_y).reshape(-1, 1)\n",
    "        val_y = onehot_encoder.fit_transform(val_y)\n",
    "        print('预测的概率矩阵为：')\n",
    "        print(test_pred)\n",
    "        test += test_pred\n",
    "        score=abs_sum(val_y, val_pred)\n",
    "        cv_scores.append(score)\n",
    "        print(cv_scores)\n",
    "    print(\"%s_scotrainre_list:\" % clf_name, cv_scores)\n",
    "    print(\"%s_score_mean:\" % clf_name, np.mean(cv_scores))\n",
    "    print(\"%s_score_std:\" % clf_name, np.std(cv_scores))\n",
    "    test=test/kf.n_splits\n",
    "\n",
    "    return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-15T00:58:22.378103Z",
     "start_time": "2021-03-15T00:58:22.373222Z"
    }
   },
   "outputs": [],
   "source": [
    "def lgb_model(x_train, y_train, x_test):\n",
    "    lgb_test = cv_model(lgb, x_train, y_train, x_test, \"lgb\")\n",
    "    return lgb_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-03-15T00:53:32.384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************ 1 ************************************\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's multi_logloss: 0.0525735\n",
      "[200]\tvalid_0's multi_logloss: 0.0422444\n",
      "[300]\tvalid_0's multi_logloss: 0.0407076\n",
      "[400]\tvalid_0's multi_logloss: 0.0420398\n",
      "Early stopping, best iteration is:\n",
      "[289]\tvalid_0's multi_logloss: 0.0405457\n",
      "预测的概率矩阵为：\n",
      "[[9.99969791e-01 2.85197261e-05 1.00341946e-06 6.85357631e-07]\n",
      " [7.93287264e-05 7.69060914e-04 9.99151590e-01 2.00810971e-08]\n",
      " [5.75356884e-07 5.04051497e-08 3.15322414e-07 9.99999059e-01]\n",
      " ...\n",
      " [6.79267940e-02 4.30206297e-04 9.31640185e-01 2.81516302e-06]\n",
      " [9.99960477e-01 3.94098074e-05 8.34030725e-08 2.94638661e-08]\n",
      " [9.88705846e-01 2.14081630e-03 6.67418381e-03 2.47915423e-03]]\n",
      "[607.0736049372186]\n",
      "************************************ 2 ************************************\n",
      "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tvalid_0's multi_logloss: 0.0566626\n",
      "[200]\tvalid_0's multi_logloss: 0.0450852\n"
     ]
    }
   ],
   "source": [
    "lgb_test = lgb_model(x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-03-15T00:53:33.065Z"
    }
   },
   "outputs": [],
   "source": [
    "lgb_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-03-15T00:53:33.810Z"
    }
   },
   "outputs": [],
   "source": [
    "temp=pd.DataFrame(lgb_test)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-03-15T00:53:34.680Z"
    }
   },
   "outputs": [],
   "source": [
    "result=pd.read_csv('sample_submit.csv')\n",
    "result['label_0']=temp[0]\n",
    "result['label_1']=temp[1]\n",
    "result['label_2']=temp[2]\n",
    "result['label_3']=temp[3]\n",
    "result.to_csv('submit.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10): \n",
    "    print(len(train.values[0][1].split(',')))"
   ]
  }
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